Insights into the chemical characterization and sources of PM(2.5) in Beijing at a 1-h time resolution

Long-Term Changes in Summertime Nitrate Chemistry in the Top Boundary Layer of North China

Reducing fine particulate nitrate (pNO3-) is critical for further mitigating PM2.5 pollution in China. However, previous NOx emission reductions have failed to achieve the expected pNO3- decreases. The present study reports that pNO3- concentration in summer increased by 55.8% and 5.6% at North China Peak (1534 m a.s.l.) from 2007 to 2014 and 2014 to 2021, respectively. pNO3- formation enhancement was caused mainly by decreased aerosol acidity due to notable SO42- reduction. pNO3- formation changed from a process limited by NH4+ to one colimited by NO2 and NH4+, suggesting an increased effect of NOx reduction on decreasing pNO3- production. Vertical transport represents a significant source of pNO3- near the surface, illustrating a percentage as high as 98% recorded during daytime hours and a proportion of 34% in the dark over North China in the simulation scenario during summer 2020. The scheme to reduce NOx emissions by 10% from 2020 to 2025 is predicted to slowly decrease aloft pNO3- over North China, which may facilitate further reductions in pNO3- concentrations near the surface via vertical transport. The inflection of nitrate chemistry in the top boundary layer suggests an opportunity to accelerate PM2.5 reduction under projected further emission reductions.

Systematic evaluations of receptor models in source apportionment of particulate solids in road deposited sediments: A practical application for tracking heavy metal sources on urban road surfaces

Receptor models have been widely used to identify pollution sources in the urban environment. However, evaluating the accuracy of source apportionment results for road deposited sediments (RDS) using these models has not been the focus of previous studies. This study compared canonical receptor models, i.e., positive matrix factorization (PMF), Unmix, chemical mass balance (CMB) and chemical mass-balance based stochastic approach (SCMD) using six synthetic datasets generated from real-world source profiles, and three error evaluation indicators (ie., relative error (RE), relative prediction error (RPE), and symmetric mean absolute percentage error (SMAPE)) were employed. The SCMD model showed more stable and accurate results, with ranges from 8.48 % - 30.76 %, 16.32-32.34 %, and 7.81-24.55 % of RE, RPE, and SMAPE, respectively. SCMD was then applied for tracking Pb, Zn, Cr, Cu, Ni, and Mn on urban road surfaces in Guangzhou, China. The results showed that vehicle exhaust, tire wear, roadside soil, and brake wear contributed 50.15 %, 41.15 %, 6.84 %, and 1.86 % of the mass of particulate solids, respectively; vehicle exhaust contributed more than half of these six heavy metals, particularly Cr and Ni. These findings provide scientific support for the effective selection of appropriate receptor models for source apportionment in RDS.

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.

A novel calibration method for continuous airborne metal measurements: Implications for aerosol source apportionment

Continuous metal monitors have been widely used in environmental monitoring due to the high temporal resolution, high detection limit, and necessity for near real-time source apportionment. However, the reliability of the conventional calibration method, the deviation caused by uncalibrated monitoring data, and the subsequent impact on source identification results are rarely discussed. In this study, a reliable multi-point calibration approach by Primary Standard Aerosol Mass Concentration Calibration System (PAMAS) for the Xact625i Ambient Metals Monitor was developed and applied. The measured data was almost meaningless in the low-concentration range with bias even exceeding 100 % by using the conventional single-point calibration method based on thin-film standards. PAMAS was utilized to generate aerosols with known concentrations of the 20 metal elements including Al, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Sr, Cd, Sn, Sb, Ba, Tl, Pb, and Bi, in two concentration ranges of 150-1200 ng m-3 and 2.5-30 ng m-3 to validate the Xact625i Monitor. The results showed that the elemental concentrations were underestimated, especially in the low-concentration range, only for Cr, As, and Sr with slopes close to unity (1.00 ± 0.03). After calibration by PAMAS, the slopes of the linear relationships between measured and standard concentrations were all unity for the 19 elements in the high-concentration range, and close to unity for the 15 elements in the low-concentration range, and the accuracy of the remaining elements was also improved. After considering the calibration of aerosol metal data, it was found the number of source factors and their contributions to metals and PM2.5 in Chongming Dongtan, China, based on the PMF model significantly changed. This study highlighted the need of developing reliable calibration methods for online aerosol monitoring instruments and implied that the source apportionment results could be biased without careful data calibration.

High-spatiotemporal-resolution mapping of PM2.5 traffic source impacts integrating machine learning and source-specific multipollutant indicator

Traffic sources are a major contributor to fine particulate matter (PM2.5) pollution, with their emissions and diffusion exhibiting complex spatiotemporal patterns. Receptor models have limitations in estimating high-resolution source contributions due to insufficient observation networks of PM2.5 compositions. This study developed a source apportionment method that integrates machine learning and emission-based integrated mobile source indicator (IMSI) to rapidly and accurately estimate PM2.5 traffic source impacts with high spatiotemporal resolution in the Beijing-Tianjin-Hebei region. Firstly, we utilized multisource data and developed various machine learning models to optimize the traffic-related pollutant concentration fields simulated by a chemical transport model. Results demonstrated that the Extreme Gradient Boosting (XGBoost) model exhibited excellent prediction accuracy of nitrogen oxide (NO2), carbon oxide (CO), and elemental carbon (EC), with the cross-validated R values increasing to 0.87-0.92 and error indices decreasing by 50-67%. Furthermore, we estimated and predicted daily mappings of PM2.5 traffic source impacts using the IMSI method based on optimized concentration fields, which improved spatially resolved source contributions to PM2.5. Our findings reveal that PM2.5 traffic source impacts display significant spatial heterogeneity, and these hotspots can be precisely identified during the pollution processes with sharp changes. The evaluation results indicated that there is a good correlation (R of 0.79) between PM2.5 traffic source impacts by IMSI method and traffic source contributions apportioned by a receptor model at Beijing site. Our study provides deeper insights of estimating the spatiotemporal distribution of PM2.5 source-specific impacts especially in regions without PM2.5 compositions, which can provide more complete and timely guidance to implement precise air pollution management strategies.

Evaluating city road dust emission characteristics with a dynamic method: A case study in Luoyang, China

Road dust, a significant contributor to non-exhaust particulate matter emissions in urban transport, poses considerable health risks, necessitating accurate and high-resolution data for effective control. The traditional AP-42 method offers data on point-specific dust emissions, while vehicle-based testing ascertains the relative emission intensity in the road network. However, a clear mathematical relationship between these measurements has been elusive, limiting efficiency in emission control. By integrating the On-board Conventional Pollutant Monitoring System with the AP-42 method, we devised a dynamic link between the concentration of particles in vehicle plumes and actual road dust emissions. This relationship is substantiated by a notable correlation (R2 = 0.91) between our emission factors and those calculated using the AP-42 method. Significant variations emerged in dust emission factors across road types, with changes between -30.1 % to +57.79 % from the average (0.05 g·vehicle-1·km-1), in tandem with traffic flow fluctuations of approximately ±90 %. Meteorological factors, except for continuous rainfall, showed minimal impact on dust emissions. However, our findings revealed a significant underestimation (58.87 %) of road dust PM10 emissions by the AP-42 method. Intriguingly, we found that short-range emission hotspots substantially contribute to total emissions, suggesting a potential 50 % reduction by controlling merely 8.8 % ± 2.5 % of the total road length. Our research elucidates the interplay between road dust emissions, road types, and human activities. The application of a dynamic, high-resolution assessment method enhances our understanding of the impacts of road dust on urban particulate pollution, allows accurate hotspot identification, and aids in developing efficacious strategies for air quality enhancement.

Source-specific health risks of PM2.5-bound toxic metals in Wuhai, a semi-arid city in northwest China

Quantifying the individual impact of each PM2.5-containing source on increasing health risk is essential for mitigating the harmful effects of atmospheric pollutants to human health. However, there remains a limited understanding of these health risks and their association with sources in semi-arid cities. To address this lack of understanding, 20 PM2.5-bound toxic metals (PTMs) were observed at six sampling sites in Wuhai, a typical semi-arid city in northwest China. The spatiotemporal variations, sources, and health risks of PTMs in Wuhai were investigated. Silicon (Si), Ca, Na, Al, Mg, and Fe were the predominant metals, accounting for 90.2 % of total metals. The contents of anthropogenic metals (Cd, Hg, and Pb) were higher during winter and autumn, whereas those of crustal metals (Si, Fe, Cu, Al, and Co) were higher during spring. The sources of PTMs in Wuhai were identified as soil sources (SS, 57.8 %), fugitive dust (FD, 23.3 %), vehicular emissions (VE, 13.4 %), metal smelting (MS, 3.9 %), and coal combustion (CC, 1.7 %). The hazard quotient of Mn and the hazard index of PTMs for children were >1, suggesting a non-carcinogenic health risk for children. The carcinogenic risk of Cr was >1 × 10-6, suggesting a Cr-associated carcinogenic risk for both adults and children in Wuhai. The main sources of non-carcinogenic risk included VE (62.5 %), SS (18.4 %), MS (11.3 %), CC (4.6 %), and FD (3.2 %). Alternatively, the main sources of carcinogenic risks included MS (53.8 %), VE (15.9 %), SS (13.9 %), CC (8.8 %), and FD (7.6 %). Overall, this study suggests that natural sources (SS and FD) are significant contributors to the health risks of PTMs in semi-arid cities. In conclusion, this study provides a comprehensive understanding of PTMs in semi-arid cities and reveals the contribution of each potential source to corresponding health risks.

Effects of SO2 on NH4NO3 Photolysis: The Role of Reducibility and Acidic Products

Nitrate photolysis is a vital process in secondary NOx release into the atmosphere. The heterogeneous oxidation of SO₂ due to nitrate photolysis has been widely reported, while the influence of SO₂ on nitrate photolysis has rarely been investigated. In this study, the photolysis of nitrate on different substrates was investigated in the absence and presence of SO₂. In the photolysis of NH₄NO₃ on the membrane without mineral oxides, NO, NO₂, HONO, and NH₃ decreased by 17.1, 6.0, 12.6, and 57.1% due to the presence of SO₂, respectively. In the photolysis of NH₄NO₃ on the surface of mineral oxides, SO₂ also exhibited an inhibitory effect on the production of NOx, HONO, and NH₃ due to its reducibility and acidic products, while the increase in surface acidity due to the accumulation of abundant sulfate on TiO₂ and MgO promoted the release of HONO. On the photoactive oxide TiO₂, HSO₃^-, generated by the uptake of SO₂, could compete for holes with nitrate to block nitrate photolysis. This study highlights the interaction between the heterogeneous oxidation of SO₂ and nitrate photolysis and provides a new perspective on how SO₂ affects the photolysis of nitrate absorbed on the photoactive oxides.

Oxidative Potential Characterization of Different PM2.5 Sources and Components in Beijing and the Surrounding Region

With the implementation of air pollution control measures, the concentration of air pollutants in the North China Plain has exhibited a downward trend, but severe fine particulate matter (PM_2.5) pollution remains. PM_2.5 is harmful to human health, and the exploration of its source characteristics and potential hazards has become the key to mitigating PM_2.5 pollution. In this study, PM_2.5 samples were collected in Beijing and Gucheng during the summer of 2019. PM_2.5 components, its oxidative potential (OP), and health risks were characterized. The average PM_2.5 concentrations in Beijing and Gucheng during the sampling period were 34.0 ± 6.1 μg/m³ and 37.1 ± 6.9 μg/m³, respectively. The principal component analysis (PCA) results indicated that the main sources of PM_2.5 in Beijing were vehicle exhaust and secondary components and that the main sources in Gucheng were industrial emissions, dust and biomass combustion. The OP values were 91.6 ± 42.1 and 82.2 ± 47.1 pmol/(min·m³), respectively, at these two sites. The correlation between the chemical components and the OP values varied with the PM_2.5 sources at these two locations. The health risk assessment results demonstrated that Cr and As were potentially carcinogenic to all populations at both sites, and Cd posed a potential carcinogenic risk for adults in Gucheng. Regional cooperation regarding air pollution control must be strengthened to further reduce PM_2.5 pollution and its adverse health effects.

Atmospheric ammonia in the rural North China Plain during wintertime: Variations, sources, and implications for HONO heterogeneous formation

Atmospheric ammonia (NH₃) plays an important role in secondary inorganic aerosol formation. Understanding the temporal variations, sources, and environmental influences of NH₃ is conducive to better formulate PM_2.5 pollution control strategies for policy-makers. Here, we performed a comprehensive field campaign with the measurements of NH₃ and related parameters at a rural site of the North China Plain (NCP) in winter of 2017. The results showed that residential coal combustion contributed dominantly to NH₃ during the entire observation period, resulting in the obviously high average concentration of NH₃ (31.2 ± 24.6 ppbv). The sensitivity tests of pH-NH_x during the three different pollution periods suggested that the rural site was always in the NH_x-rich atmosphere where high levels of NH_x increased the particle pH inefficiently. Nevertheless, the particle pH still elevated by 1.5-2.2 units at the excessive NH_x levels during the three pollution periods. In addition, the HONO/NO₂ ratios were found to correlate linearly with NH₃ concentrations, implying the acceleration effect of NH₃ on HONO production from NO₂ heterogeneous reactions. After considering the NH₃-enhanced uptake coefficient of NO₂ in the nocturnal HONO budget, the unknown source of HONO could be fully explained. Therefore, more attentions should be given for effective emission control of NH₃ to improve air quality throughout the NCP, especially in the rural areas.

Highly time-resolved measurements of elements in PM2.5 in Changzhou, China: Temporal variation, source identification and health risks

The temporal variation, sources, and health risks of elemental composition in fine particles (PM_2.5) were explored using online measurements of 19 elements with a time resolution of 1 h at an urban location in Changzhou, China, from December 10, 2020 to March 31, 2021. The mass concentration of PM_2.5 was 50.1 ± 32.6 μg m^-3, with a range of 3-218 μg m^-3. The total concentration of 19 elements (2568 ± 1839 ng m^-3) accounted for 5.1 % of PM_2.5 mass concentration. S, Cl, Si, and Fe were the dominant elementary species, accounting for 90 % of total element mass concentrations during the whole campaign. Positive matrix factorization (PMF) model was applied to identify the major emission sources of elements in PM_2.5. Seven factors, named secondary sulfate mixed with coal combustion, Cl-rich, traffic, iron and steel industry, soil dust, fireworks, and shipping, were identified. The major sources for elements were iron and steel industry, followed by soil dust and secondary sulfate mixed with coal combustion, explaining 32.0 %, 23.5 % and 16.7 % of the total source contribution, respectively. The total hazard index (HI) of elements was 3.01 for children and 1.18 for adults, much greater than the admissible level (HI = 1). The total carcinogenic risk (CR) in Changzhou was estimated to be 5.87 × 10^-5, which was above the acceptable CR level (1 × 10^-6). Among the calculated metal elements, Cr, Co and As have higher carcinogenic risk, and Co was found to trigger the highest noncarcinogenic risk to Children. Our results indicate that industrial emission is the dominant CR contributor, emphasizing the necessity for stringent regulation of industry sources. Overall, our study provides useful information for policymakers to reduce emissions and health risks from elements in the Yangtze River Delta region.

Identifying and quantifying PM2.5 pollution episodes with a fusion method of moving window technique and constrained Positive Matrix Factorization

PM_2.5 pollution episodes rapidly and significantly deteriorate the air quality and are a critical concern worldwide. This study developed a fusion method based on the moving window dataset technique and constrained Positive Matrix Factorization (PMF) to differentiate and characterize potential factors in a PM_2.5 episode case assuming having one new contributor. The hourly PM_2.5 compositions of elements, ions and carbonaceous components, were collected from September to December 2020 in Taipei, Taiwan. Constraint targets based on the bootstrap analysis result of a PMF model using a long-term input dataset were imposed on the modeling of each moving window to ensure similar features of the retrieved factors. The constituents of an additionally differentiated factor to the episode, which was identified as regional transport, were stable among each moving window that covered the occurrence of the episode as revealed by the profile matching index. The results showed that the largest contributor to the PM_2.5 mass during the episode period of 12/12/2020 was regional transport (61%), whereas that of 12/13 was the regular pollution of industry/ammonium sulfate related (43%). According to our review of the literature, this study is the first to apply both the moving window technique and constrained PMF to characterize the episode. The findings provide valuable information that can be used to explore the causes of PM_2.5 episodes and implement air pollution control strategies.

Evaluating the influence of constant source profile presumption on PMF analysis of PM2.5 by comparing long- and short-term hourly observation-based modeling

Hourly PM_2.5 speciation data have been widely used as an input of positive matrix factorization (PMF) model to apportion PM_2.5 components to specific source-related factors. However, the influence of constant source profile presumption during the observation period is less investigated. In the current work, hourly concentrations of PM_2.5 water-soluble inorganic ions, bulk organic and elemental carbon, and elements were obtained at an urban site in Nanjing, China from 2017 to 2020. PMF analysis based on observation data during specific pollution (firework combustion, sandstorm, and winter haze) and emission-reduction (COVID-19 pandemic) periods was compared with that using the whole 4-year data set (PMF_whole). Due to the lack of data variability, event-based PMF solutions did not separate secondary sulfate and nitrate. But they showed better performance in simulating average concentrations and temporal variations of input species, particularly for primary source markers, than the PMF_whole solution. After removing event data, PMF modeling was conducted for individual months (PMF_month) and the 4-year period (PMF_4-year), respectively. PMF_month solutions reflected varied source profiles and contributions and reproduced monthly variations of input species better than the PMF_4-year solution, but failed to capture seasonal patterns of secondary salts. Additionally, four winter pollution days were selected for hour-by-hour PMF simulations, and three sample sizes (500, 1000, and 2000) were tested using a moving window method. The results showed that using short-term observation data performed better in reflecting immediate changes in primary sources, which will benefit future air quality control when primary PM emissions begin to increase.

Decade-long trends in chemical component properties of PM2.5 in Beijing, China (2011-2020)

A 10-year-long measurement of water-soluble inorganic ions in PM_2.5 was made in Beijing from June 2011 to December 2020, to investigate the interannual trends of chemical characteristics of PM_2.5 and to provide insights into the future prevention and control of PM_2.5 pollution. From 2011 to 2020, with the implementation of strict air pollution control strategies, significant changes of PM_2.5 have been observed in Beijing, with NO₃^-, SO₄^2- and NH₄⁺ decreasing at rates of 5.10, 8.80 and 7.64% yr^-1 respectively. The percentages of NO₃^- and SO₄^2- under elevated pollution levels were investigated. When PM_2.5 values fell in the range of 0-400 μg m^-3, NO₃^-/ SO₄^2- values were mostly higher than 1 and showed upward trends from 2011 to 2020. However, under extremely heavy haze conditions, SO₄^2- dominated PM_2.5 formation. This result was closely related to the change characteristics of the oxidation ratio of sulfate (SOR), the oxidation ratio of nitrate (NOR) and gaseous precursors under different pollution levels. The change characteristics of NOR and SOR under elevated PM_2.5 levels indicated that the aqueous phase oxidation was the key process driving SO₄^2- formation; while as for NO₃^-, in addition to the availability of NH₄⁺, the atmospheric oxidation capacity made crucial roles. The analysis of typical haze episodes during the past decade indicated that the emission reduction of gaseous pollutants, especially SO₂, made great contributions to the improved PM_2.5 air quality in Beijing. We highlighted that future efforts should focus on enhanced reduction of NO₂ emission and control of atmospheric oxidation capacity to further reduce particulate nitrate formation.

Assessment of the link between atmospheric dispersion and chemical composition of PM10 at 2-h time resolution

The concentration of air pollutants is governed by both emission rate and atmospheric dispersion conditions. The role played by the atmospheric mixing height in determining the daily time pattern of PM components at the time resolution of 2 h was studied during 21 days of observation selected from a 2-month field campaign carried out in the urban area of Rome, Italy. Natural radioactivity was used to obtain information about the mixing properties of the lower atmosphere throughout the day and allowed the identification of advection and stability periods. PM₁₀ composition was determined by X-ray fluorescence, ion chromatography, inductively coupled plasma-mass spectrometry and thermo-optical analysis. A satisfactory mass closure was obtained on a 2-h basis, and the time pattern of the PM₁₀ macro-sources (soil, sea, secondary inorganics, organics, traffic exhaust) was acquired at the same time scale. After a complete quality control procedure, 27 main components and source tracers were selected for further elaboration. On this database, we identified some groups of co-varying species related to the main sources of PM. Each group showed a peculiar behaviour in relation to the mixing depth. PM components released by soil, biomass burning and traffic exhaust, and, particularly, ammonium nitrate, showed a clear dependence on the mixing properties of the lower atmosphere. Biomass burning components and organics peaked during the night hours (around midnight), following the atmospheric stabilization and increased emission rate. Traffic exhausts and non-exhausts species also peaked in the evening, but they showed a second, minor increase between 6:00 and 10:00 when the strengthening of the emission rate (morning rush hour) was counterbalanced by the dilution of the atmosphere (increasing mixing depth). In the case of ammonium nitrate, high concentrations were kept during the whole night and morning.

Refined source apportionment of residential and industrial fuel combustion in the Beijing based on real-world source profiles

Residential and industrial emissions are considered as dominant contributors to ambient fine particulate matter (PM_2.5) in China. However, the contributions of residential and industrial fuel combustion are difficult to distinguish because specific source indicators are lacking. In this study, real-world source testing was performed on residential coal, biomass and industrial combustion, industrial processes, and diesel and gasoline vehicle source emissions in the Beijing-Tianjin-Hebei region, China. PM_2.5 emission factors and chemical profiles, including 97 compositions (e.g., carbonaceous matter, water-soluble ions, elements, EPA priority polycyclic aromatic hydrocarbons (EPAHs), methyl PAHs (MPAHs), and n-alkanes) were obtained for the aforementioned sources. The results showed high OC1, OC2, fluoranthene, methyl fluoranthene, and retene in emissions from residential coal combustion, high OC3, sulfate, Ca, and iron abundance in emissions from industrial combustion, and high Pb and Zn loadings in emissions from industrial processes. Furthermore, specific diagnostic ratios were determined to distinguish between residential and industrial fuel combustion. For example, the ratios of MPAHs/EPAHs (>1) and Mfluo/Fluo (>5) can be used as fingerprinting ratios to distinguish residential coal combustion from other sources. Finally, 1-h resolution refined source apportionments of PM_2.5 were conducted in Beijing during two haze events (EP1 and EP2) with a chemical mass balance (CMB) model based on the localized real-world source profiles established in this study. Source apportionment results of CMB showed that the contributions of industrial and residential fuel combustion were 19.4% and 30.8% in EP1 and 26.8% and 18.1% in EP2, respectively, which were comparable to the results of the positive matrix factorization model (R² = 0.82). This study provides valuable information for the successful and accurate determination of the contributions of residential and industrial fuel combustion to ambient PM_2.5.

A hybrid methodology to quantitatively identify inorganic aerosol of PM2.5 source contribution

It is difficult to identify inorganic aerosol (IA) (primary and secondary), the main component of PM_2.5, without the significant tracers for sources. We are not aware of any studies specifically related to the IA's local contribution to PM_2.5. To effectively reduce the IA load, however, the contribution of local IA sources needs to be identified. In this work, we developed a hybrid methodology and applied online measurement of PM_2.5 and the associated compounds to (1) classify local and long-range transport PM_2.5, (2) identify sources of local PM_2.5 using PMF model, and (3) quantify local source contribution to IA in PM_2.5 using regression analysis. Coal combustion and iron ore and steel industry contributed the most amount of IA (~42.7%) in the study area (City of Taichung), followed by 32.9% contribution from oil combustion, 8.9% from traffic-related emission, 4.6% from the interactions between agrochemical applications and combustion sources (traffic-related emissions and biomass burning), and 2.3% from biomass burning. The methodology developed in this study is an important preliminary step for setting up future control policies and regulations, which can also be applied to any other places with serious local air pollution.

Accurate real-time monitoring of fine dust using a densely connected convolutional networks with measured plasma emissions

Accurate identification and monitoring of fine dust are emerging as a primary global issue for addressing the harmful effects of fine dust on public health. Identifying the source of fine dust is indispensable for ensuring the human lifespan as well as preventing environmental disasters. Here a simple yet effective spark-induced plasma spectroscopy (SIPS) unit combined with deep learning for real-time classification is verified as a fast and precise PM (particulate matter) source identification technique. SIPS promises portable use, label-free detection, source identification, and chemical susceptibility in a single step with acceptable speed and accuracy. In particular, the densely connected convolutional networks (DenseNet) are used with measured spark-induced plasma emission datasets to identify PM sources at above 98%. The identification performance was compared with other common classification methods, and DenseNet with dropouts (30%), optimized batch size (16), and cyclic learning rate training emerged as the most promising source identification method.

Spatial Distribution of Primary and Secondary PM2.5 Concentrations Emitted by Vehicles in the Guanzhong Plain, China

With the rapid increase of the vehicle population in the Guanzhong Plain (GZP), the fine particulate matter (PM2.5) emitted by vehicles has an impact on regional air quality and public health. The spatial distribution of primary and secondary PM2.5 concentrations from vehicles in GZP in January and July 2017 was simulated in this study by using the Weather Research and Forecasting (WRF) model and the California Puff (CALPUFF) air quality model. The contributions of vehicle-related emission sources to total PM2.5 concentrations were also calculated. The results show that although the emissions of primary PM2.5, NOx, and SO2 in July were greater than those in January, the hourly average concentrations of primary and secondary PM2.5 in January were significantly higher than those in July. The highest concentrations of primary and total PM2.5 were mostly located in the urban areas of Xi’an and Xianyang in the central region of GZP. The contributions of exhaust emissions, secondary nitrates, brake wear, tire wear, and secondary sulfate to the total PM2.5 concentrations in GZP were 50.37%, 34.76%, 10.79%, 4.06%, and 0.04% in January and 71.91%, 11.14%, 11.89%, 5.03%, and 0.03% in July, respectively. These results will help us to further control PM2.5 pollution caused by vehicles.

Atmospheric volatile halogenated hydrocarbons in air pollution episodes in an urban area of Beijing: Characterization, health risk assessment and sources apportionment

Volatile halogenated hydrocarbons (VHCs) have attracted wide attention in the atmospheric chemistry field since they not only affect the ecological environment but also damage human health. In order to better understand the characteristics, sources and health risks of VHCs in typical urban areas in Beijing, and also verify the achievement in implementing the Montreal Protocol (MP) in Beijing, observational studies on 22 atmospheric VHCs species were conducted during six air pollution episodes from December 2016 to May 2017. The range in daily mixing ratios of the 6 MP-regulated VHCs was 1000-1168 pptv, and the 16 MP-unregulated VHCs was 452-2961 pptv. The 16 MP-unregulated VHCs accounted for a relatively high concentration proportion among the 22 VHCs with a mean of 70.25%. Compared with other regions, the mixing ratios of MP-regulated VHCs were in the middle concentrations. The mixing ratios of the MP-regulated VHCs remained the same concentrations during the air pollution episodes, while the concentrations of MP-unregulated VHCs were generally higher on polluted days than on clean days and increased with the aggravation of the pollution episodes. The mixing ratios of dichlorodifluoromethane and trichlorofluoromethane were higher than Northern Hemisphere (NH) background values, while the mixing ratios of the other 4 MP-regulated VHCs were moderate and similar to the NH background values. All the 9 VHCs with carcinogenic risk might pose potential carcinogenic risks to the exposed populations in the six pollution episodes, while none of the 12 VHCs might pose appreciable non-carcinogenic risks to the exposed populations. Considering the higher concentration levels and higher risk values of 1,2-dichloropropane, 1,2-dichloroethane, carbon tetrachloride and trichloromethane, Beijing needs to further strengthen the control of these VHCs. The analysis of air mass transportation and PMF model showed that regional transportation and leakage of CFCs banks were important sources of VHCs in Beijing, and the contribution of industrial process and solvent usage should not be neglected. The results revealed the effective implementation of the MP in Beijing and its surrounding areas, while further measures are suggested to control the emissions of important VHCs especially from regional transportation and leakage of CFCs banks to reduce the possible health risks to the exposed population.

Source apportionment of PM2.5 during different haze episodes by PMF and random forest method based on hourly measured atmospheric pollutant

Hourly measured PM2.5-bound species, gases, and meteorological data were analyzed by the PMF receptor model to quantify source contributions, and by the random forest to estimate decisive factors of variations of PM2.5, sulfur oxidation ratio (SOR), and nitrogen oxidation ratio (NOR) during different haze episodes. PM2.5 variation was influenced by CO (17%), SO2 (19%), NH3 (12%), O3 (10%), air pressure (P, 9.9%), and temperature (T, 10%) during the whole period. SOR was determined by SO2 (15%), temperature (T, 9.8%), relative humidity (RHU, 15%), and pondus hydrogenii (pH, 35%), and NOR was influenced by NOx (19%), O3 (14%), NH3 (13%), and RHU (15%). Three types of pollution episodes were captured. Process I was characterized by high CO (contributing 40% of PM2.5 concentration variation estimated by the random forest) due to coal combustion for heating during winter in northern China. According to the PMF, coal combustion (32%) and secondary sources (38%) were both the most important contributors in the first stage, and then, when the RHU increased to above 80%, the highest contribution was from secondary sources (40%). Process II was during the Spring Festival and was characterized by 8.8 μg m-3 firework contribution. High SO2 during this process, especially on the CNY's Eve, was observed due to the firework displays, and SO2 gave a high contribution (24%) to PM2.5 variation. Process III showed high ions and high RHU in summer with sulfate and nitrate contributing 44% and 22%, respectively. Furthermore, meteorological parameters and NH3 play a key role on SOR and NOR.

Nonlinear response of nitrate to NOx reduction in China during the COVID-19 pandemic

In recent years, nitrate plays an increasingly important role in haze pollution and strict emission control seems ineffective in reducing nitrate pollution in China. In this study, observations of gaseous and particulate pollutants during the COVID-19 lockdown, as well as numerical modelling were integrated to explore the underlying causes of the nonlinear response of nitrate mitigation to nitric oxides (NO_x) reduction. We found that, due to less NO_x titration effect and the transition of ozone (O₃) formation regime caused by NO_x emissions reduction, a significant increase of O₃ (by ∼ 69%) was observed during the lockdown period, leading to higher atmospheric oxidizing capacity and facilitating the conversion from NO_x to oxidation products like nitric acid (HNO₃). It is proven by the fact that 26-61% reduction of NO_x emissions only lowered surface HNO₃ by 2-3% in Hebi and Nanjing, eastern China. In addition, ammonia concentration in Hebi and Nanjing increased by 10% and 40% during the lockdown, respectively. Model results suggested that the increasing ammonia can promote the gas-particle partition and thus enhance the nitrate formation by up to 20%. The enhanced atmospheric oxidizing capacity together with increasing ammonia availability jointly promotes the nitrate formation, thereby partly offsetting the drop of NO_x. This work sheds more lights on the side effects of a sharp NO_x reduction and highlights the importance of a coordinated control strategy.

Evaluating carbon content in airway macrophages as a biomarker of personal exposure to fine particulate matter and its acute respiratory effects

It remains unclear whether carbon content in airway macrophages (AM) can predict personal short-term exposure to fine particulate matter (PM_2.5) air pollution and its respiratory health effects. We aimed to evaluate the pathway from personal PM_2.5 exposure to adverse respiratory outcomes through AM carbon content. We designed a longitudinal panel study with 3 scheduled follow-ups among 113 non-smoking patients of chronic obstructive pulmonary disease in Shanghai, China, from April 2017 to January 2019. We quantified AM carbon content from induced sputum by image analysis, tested lung function and measured sputum levels of 4 pro-inflammatory cytokines and 2 anti-inflammatory cytokines. We applied the "meet in the middle" approach incorporating linear mixed-effect models to evaluate the associations from external PM_2.5 exposure to respiratory outcomes through AM carbon content. Our results indicated that personal exposure to PM_2.5 within 24 h was significantly associated with decreased forced expiratory volume in 1s and anti-inflammatory cytokines, as well as increased macrophages and pro-inflammatory cytokines. These changes were accompanied by increased areas of AM carbon and higher percentage of AM area occupied by carbon, both of which were associated with increased levels of pro-inflammatory cytokines and decreased levels of anti-inflammatory cytokines. Exposure to ambient black carbon and organic carbon in PM_2.5 within 2 days was significantly associated with increased AM carbon area and percentage of AM area occupied by carbon. Our findings reinforced the causality in respiratory health effects of PM_2.5 in which increased AM carbon content might serve as a valid exposure biomarker.

Impacts of COVID-19 on Black Carbon in Two Representative Regions in China: Insights Based on Online Measurement in Beijing and Tibet

Under the influence of Coronavirus Disease 2019 (COVID-19), China conducted a nationwide lockdown (LD) which significantly reduced anthropogenic emissions. To analyze the different impacts of COVID-19 on black carbon (BC) in the two representative regions in China, one-year continuous online measurements of BC were conducted simultaneously in Beijing and Tibet. The average concentration in the LD period was 20% higher than that in the pre-LD period in Beijing, which could be attributed to the increase of transport from southwestern neighboring areas and enhanced aged BC. In contrast to megacity, the average concentration of BC in Tibet decreased over 70% in the LD period, suggesting high sensitivity of plateau background areas to the anthropogenic emission reduction in South Asia. Our study clearly showed that BC responded very differently in megacity and background areas to the change of anthropogenic emission under the lockdown intervention.

High-time-resolution PM2.5 source apportionment based on multi-model with organic tracers in Beijing during haze episodes

Fine particulate matter (PM_2.5) is a prominent atmospheric pollutant that poses serious adverse effects on air quality and human health. PM_2.5 source apportionment based on receptor model suggests that Beijing is polluted by mixed emission sources, but the model is limited by a lack of organic tracers and an inability to distinguish between contributions from local and regional transport. In this study, positive matrix factorization (PMF) model with organic tracers was employed to analyze refined PM_2.5 pollution sources at 1-h time resolution, and the contribution of regional transport was quantified using Particulate source apportionment technology (PSAT) in the Comprehensive Air Quality Model with Extensions (CAMx). The results identified nine source types using PMF model based on offline data for PM_2.5 concentrations, organic carbon, elemental carbon, water-soluble ions, trace elements and organic species. Gasoline and diesel exhausts were distinguished by adding polycyclic aromatic hydrocarbons (PAHs), C19-C24 n-alkanes as key organic tracers. In addition, levoglucosan and hexadecanoic acid are important additions for identifying biomass burning and cooking, respectively. Furthermore, the contribution of specific sources and source regions, from the formation to dissipation of two typical haze episodes (EP1 and EP2) in Beijing, was quantitatively analyzed. EP1 was primarily caused by local emissions with an average contribution rate of 67.5%, characterized by secondary source, gasoline and diesel exhausts, as well as industrial source. EP2 was dominated by secondary source from regional transport contributing approximately 50%. Short-range transport from Baoding (9.1%) and Langfang (5.8%) in Hebei Province was the largest external contributor, and long-range transport contributed 20% of the PM_2.5 concentration. This study suggests that combining receptor model-based source apportionment with air quality model has practical significance for understanding the causes of haze episodes, setting city-specific emission reduction measures and improving air quality in the Beijing-Tianjin-Hebei (BTH) region.

High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM2.5 Distribution in Beijing, China

PM2.5 is one of the primary components of air pollutants, and it has wide impacts on human health. Land use regression models have the typical disadvantage of low temporal resolution. In this study, various point of interests (POIs) variables are added to the usual predictive variables of the general land use regression (LUR) model to improve the temporal resolution. Hourly PM2.5 concentration data from 35 monitoring stations in Beijing, China, were used. Twelve LUR models were developed for working days and non-working days of the heating season and non-heating season, respectively. The results showed that these models achieved good fitness in winter and summer, and the highest R2 of the winter and summer models were 0.951 and 0.628, respectively. Meteorological factors, POIs, and roads factors were the most critical predictive variables in the models. This study also showed that POIs had time characteristics, and different types of POIs showed different explanations ranging from 5.5% to 41.2% of the models on working days or non-working days, respectively. Therefore, this study confirmed that POIs can greatly improve the temporal resolution of LUR models, which is significant for high precision exposure studies.

Characteristics and sources of hourly elements in PM10 and PM2.5 during wintertime in Beijing

Characteristics and sources of ambient particle elements in urban Beijing were studied by hourly observations in two size fractions (PM₁₀ and PM_2.5) during November and December 2017 using an online multi-element analyzer. The reconstructed oxide concentrations of 24 elements (from Al to Pb) comprise an appreciable fraction of PM₁₀ and PM_2.5, accounting for 37% and 17%, respectively on average. We demonstrate the benefit of using high-time-resolution chemical speciation data in achieving robust source apportionment of the total elemental PM₁₀ (PM_10el) and elemental PM_2.5 (PM_2.5el) mass using positive matrix factorization (PMF). Biomass burning, coal combustion, secondary sulfate, industry, non-exhaust traffic and dust were identified in both size fractions (with varying relative concentrations), which accounted on average for 4%, 12%, 5%, 2%, 14%, and 63%, respectively to the total PM_10el, and 14%, 35%, 21%, 6%, 12% and 12%, respectively to the total PM_2.5el. Biomass burning and coal combustion exhibited higher concentrations during haze episodes of the heating season. In contrast, secondary sulfate and industry contributed more to haze episodes during the non-heating season. The fractional contribution of dust was mostly high during clean days, while the fractional non-exhaust traffic emission contribution was similar throughout the measurement period. The non-exhaust traffic emissions contributed locally, while the remaining sources were dominated by neighboring areas. Furthermore, trajectory analysis showed that the origin of the industrial sources roughly agreed with the locations of the main point sources. Overall, this work provides detailed information on the characteristics of the elements during different haze events during heating and non-heating seasons.

Large-Scale Spraying of Roads with Water Contributes to, Rather Than Prevents, Air Pollution

Spraying roads with water on a large scale in Chinese cities is one of the supplementary precaution or mitigation actions implemented to control severe air pollution events or heavy haze-fog events in which the mechanisms causing them are not yet fully understood. These air pollution events were usually characterized by higher air humidity. Therefore, there may be a link between this action and air pollution. In the present study, the impact of water spraying on the PM_2.5 concentration and humidity in air was assessed by measuring chemical composition of the water, undertaking a simulated water spraying experiment, measuring residues and analyzing relevant data. We discovered that spraying large quantities of tap or river water on the roads leads to increased PM_2.5 concentration and humidity, and that daily continuous spraying produces a cumulative effect on air pollution. Spraying the same amount of water produces greater increases in humidity and PM_2.5 concentration during cool autumn and winter than during hot summer. Our results demonstrate that spraying roads with water increases, rather than decreases, the concentration of PM_2.5 and thus is a new source of anthropogenic aerosol and air pollution. The higher vapor content and resultant humidity most likely create unfavorable meteorological conditions for the dispersion of air pollution in autumn and winter with low temperature.

Particulates induced lung inflammation and its consequences in the development of restrictive and obstructive lung diseases: a systematic review

Particulate matters (PMs) are significant components of air pollution in the urban environment. PMs with aerodynamic diameter less than 2.5 μm (PM_2.5) can penetrate to the alveolar area and introduce numerous compounds to the pneumocystis that can initiate inflammatory response. There are several questions about this exposure as follows: does PM_2.5-induced inflammation lead to a specific disease? If yes, what is the form of the progressed disease? This systematic review was designed and conducted to respond to these questions. Four databases, including Web of Science, Scopus, PubMed, and Embase, were reviewed systematically to find the related articles. According to the included articles, the only available data on the inflammatory effects of PM_2.5 comes from either in vitro or animal studies. Both types of studies have shown that the induced inflammation is type I and includes secretion of proinflammatory cytokines. The exposure duration of longer than 28 weeks was not observed in any of the reviewed studies. However, as there is not a specific antigenic component in the urban particulate matters and based on the available evidence, the antigen-presenting is not a common process in the inflammatory responses to PM_2.5. Therefore, neither signaling to repair cells such as fibroblasts nor over-secretion of extracellular matrix (ECM) proteins can occur following PM_2.5-induced inflammation. These pieces of evidence weaken the probability of the development of fibrotic diseases. On the other hand, permanent inflammation induces the destruction of ECM and alveolar walls by over-secretion of protease enzymes and therefore results in progressive obstructive effects.

Reapportioning the sources of secondary components of PM2.5: A combined application of positive matrix factorization and isotopic evidence

Secondary particles account for a considerable proportion of fine particles (PM_2.5) and reasonable reapportioning them to primary sources is critical for designing effective strategies for air quality improvement. This study developed a method which can reapportion secondary sources of PM_2.5 solved by positive matrix factorization (PMF) to primary sources based on the isotopic signals of nitrate, ammonium and sulfate. Actual PM_2.5 data in Beijing were used as a case study to assess the feasibility and capacity of this method. In the case, 20 chemical components were used to apportion PM_2.5 sources and source contributions of nitrate were applied to reapportion secondary source to primary sources. The model performance was also estimated by radiocarbon measurement (¹⁴C) of organic (OC) and elemental (EC) carbons of eight samples. The PMF apportioned seven sources: the secondary source (36.1%), vehicle exhausts (18.7%), industrial sources (13.6%), biomass burning (11.4%), coal combustion (8.10%), construction dust (7.93%) and fuel oil combustion (4.24%). After the reapportionment of the secondary source, vehicle exhausts (28.7%) contributed the most to PM_2.5, followed by biomass burning (25.1%) and industrial sources (18.9%). Fossil oil combustion and coal combustion increased to 8.00% and 11.4%, respectively, and construction dust contributed the least. The average gap between contributions of identified sources to OC and EC and the ¹⁴C measurements decreased 2.5 ± 1.2% after the reapportionment than 13.2 ± 10.8%, indicating the good performance of the developed method.

Review of online source apportionment research based on observation for ambient particulate matter

Particulate matter source apportionment (SA) is the basis and premise for preventing and controlling haze pollution scientifically and effectively. Traditional offline SA methods lack the capability of handling the rapid changing pollution sources during heavy air pollution periods. With the development of multiple online observation techniques, online SA of particulate matter can now be realized with high temporal resolution, stable and reliable continuous observation data on particle compositions. Here, we start with a summary of online measuring instruments for monitoring particulate matters that contains both online mass concentration (online MC) measurement, and online mass spectrometric (online MS) techniques. The former technique collects ambient particulate matter onto filter membrane and measures the concentrations of chemical components in the particulate matter subsequently. The latter technique could be further divided into two categories: bulk measurement and single particle measurement. Aerosol Mass Spectrometers (AMS) could provide mass spectral information of chemical components of non-refractory aerosols, especially organic aerosols. While the emergence of single-particle aerosol mass spectrometer (SPAMS) technology can provide large number of high time resolution data for online source resolution. This is closely followed by an overview of the methods and results of SA. However, online instruments are still facing challenges, such as abnormal or missing measurements, that could impact the accuracy of online dataset. Machine leaning algorithm are suited for processing the large amount of online observation data, which could be further considered. In addition, the key research challenges and future directions are presented including the integration of online dataset from different online instruments, the ensemble-trained source apportionment approach, and the quantification of source-category-specific human health risk based on online instrumentation and SA methods.

Characteristics of PM2.5 pollution in Beijing after the improvement of air quality

Following the implementation of the strictest clean air policies to date in Beijing, the physicochemical characteristics and sources of PM_2.5 have changed over the past few years. To improve pollution reduction policies and subsequent air quality further, it is necessary to explore the changes in PM_2.5 over time. In this study, over one year (2017-2018) field study based on filter sampling (TH-150C; Wuhan Tianhong, China) was conducted in Fengtai District, Beijing, revealed that the annual average PM_2.5 concentration (64.8 ± 43.1 μg/m³) was significantly lower than in previous years and the highest PM_2.5 concentration occurred in spring (84.4 ± 59.9 μg/m³). Secondary nitrate was the largest source and accounted for 25.7% of the measured PM_2.5. Vehicular emission, the second largest source (17.6%), deserves more attention when considering the increase in the number of motor vehicles and its contribution to gaseous pollutants. In addition, the contribution from coal combustion to PM_2.5 decreased significantly. During weekends, the contribution from EC and NO₃^- increased whereas the contributions from SO₄^2-, OM, and trace elements decreased, compared with weekdays. During the period of residential heating, PM_2.5 mass decreased by 23.1%, compared with non-heating period, while the contributions from coal combustion and vehicular emission, and related species increased. With the aggravation of pollution, the contribution of vehicular emission and secondary sulfate increased and then decreased, while the contribution of NO₃^- and secondary nitrate continued to increase, and accounted for 34.0% and 57.5% of the PM_2.5 during the heavily polluted days, respectively.

Temporal variation characteristics and source apportionment of metal elements in PM2.5 in urban Beijing during 2018-2019

To explore high-resolution temporal variation characteristics of atmospheric metal elements concentration and more accurate pollution sources apportionment, online monitoring of metal elements in PM_2.5 with 1-h time resolution was conducted in Beijing from August 22, 2018 to August 21, 2019. Concentration of 18 elements varied between detection limit (ranging from 0.1 to 100 ng/m³) and nearly 25 μg/m³. Si, Fe, Ca, K and Al represented major elements and accounted for 93.47% of total concentration during the study period. Compared with previous studies, airborne metal pollution in Beijing has improved significantly which thanks to strict comprehensive control measures under the Clean Air Action Plan since 2013. Almost all elements present higher concentrations on weekdays than weekends, while concentrations of elements associated with dust sources during holidays are higher than those in working days after the morning peak, and there is almost no concentration difference in the evening peak period. Soil and dust, vehicle non-exhaust emissions, biomass, industrial processes and fuel combustion were apportioned as main sources of atmospheric metal pollution, accounting for 63.6%, 18.4%, 16.8%, 1.0% and 0.18%, respectively. Furthermore, main occurrence season of metal pollution is judged by characteristic radar chart of varied metal elements proposed for the first time in this study, for example, fuel combustion type pollution mainly occurs in winter and spring. Results of 72-h backward trajectory analysis of air masses showed that, except for local emissions, atmospheric metal pollution in Beijing is also affected by regional transport from Inner Mongolia, Hebei, the Bohai Sea and Heilongjiang.

In situ continuous hourly observations of wintertime nitrate, sulfate and ammonium in a megacity in the North China plain from 2014 to 2019: Temporal variation, chemical formation and regional transport

Nitrate (NO₃^-), sulfate (SO₄^2-) and ammonium (NH₄⁺) in airborne fine particles (PM_2.5) play a vital role in the formation of heavy air pollution in northern China. In particular, the increasing contribution of NO₃^- to PM_2.5 has attracted worldwide attention. In this study, a highly time-resolved analyzer was used to measure water-soluble inorganic ions in PM_2.5 in one of the fastest-developing megacities, Tianjin, China, from November 15 to March 15 (wintertime heating period) in 2014-2019. Severe PM_2.5 pollution episodes markedly decreased during the heating period from 2014 to 2019. The highest concentrations of NO₃^- and SO₄^2- were recorded in the heating period of 2015/2016. Afterwards, NO₃^- decreased from 2015/2016 (20.2 ± 23.8 μg/m³) to 2017/2018 (11.6 ± 14.8 μg/m³) but increased with increasing NO_x concentrations during the heating period of 2018/2019. A continuous decrease in the SO₂ concentration led to a decrease in SO₄^2- from 2015/2016 (16.8 ± 21.8 μg/m³) to 2018/2019 (6.5 ± 8.9 μg/m³). The NO₃^- and SO₄^2- concentrations increased as the air quality deteriorated. However, the proportion of NO₃^- and SO₄^2- in PM_2.5 slightly increased when the air quality deteriorated from moderate pollution (MP) to severe pollution (SP) levels. The average molar ratios of NH₄⁺ to [NO₃^-+2 × (SO₄^2-)] were 1.7, 0.9, 1.2, 1.2 and 1.5 for the heating periods of 2014/2015, 2015/2016, 2016/2017, 2017/2018 and 2018/2019, respectively, most of which were higher than 1.0, thus revealing an overall excess of NH₄⁺ during the heating periods. However, the molar equivalent ratios of [NH₄⁺] to [NO₃^-+2 × (SO₄^2-)] were less than 1 under increasing PM_2.5 pollution. The molar equivalent ratios of [NO₃^-]/[SO₄^2-] were positively correlated with those of [NH₄⁺]/[SO₄^2-]. When the molar equivalent ratios of [NH₄⁺]/[SO₄^2-] were more than 1.5, those of [NO₃^-]/[SO₄^2-] increased from close to 1 to higher values, indicating that the dominance of NO₃^- formation played an important role. The results of nonparametric wind regression exhibited distinct hot spots of NO₃^-, SO₄^2- and NH₄⁺ (higher concentrations) in the wind sectors between NE and SE at wind speeds of approximately 6-21 km/h. The southern areas in the North China Plain and parts of the western areas of China contributed more NO₃^-, SO₄^2- and NH₄⁺ than other areas to the study site. The abovementioned areas were also characterized by a higher contribution of NO₃^- than of SO₄^2- to the study site and by NH₄⁺-rich conditions. In summary, more efforts should be made to reduce NO_x in the Beijing-Tianjin-Hebei region. This study provides observational evidence of the increasingly important role of nitrate as well as scientific support for formulating effective control strategies for regional haze in China.

High time-resolved PM2.5 composition and sources at an urban site in Yangtze River Delta, China after the implementation of the APPCAP

In this study, hourly concentrations of PM_2.5 water-soluble inorganic ions, bulk organic carbon (OC), and elemental carbon (EC) were monitored from 1/1/2017 to 12/31/2017 and validated using filter-based offline analysis at an urban site in Nanjing, China. Compared with 2013 or before, the annual average of PM_2.5 concentration (36.5 ± 32.9 μg m^-3) in 2017 decreased by more than 40%, NO₃^- (12.8 ± 11.4 μg m^-3) became the most abundant water-soluble ion instead of SO₄^2- (9.29 ± 6.07 μg m^-3), and the relative contribution of OC (5.92 ± 3.40 μg m^-3) and EC (2.95 ± 1.53 μg m^-3) to bulk PM_2.5 (24.9 ± 9.31%) increased substantially, indicating the effectiveness of the control policy for reducing gaseous precursor emissions. Based on the diurnal variations of water-soluble ions and gaseous pollutants, NH₄⁺, SO₄^2-, and NO₃^- were secondarily formed and NH₄NO₃ dominated the composition of ammonium salts in PM_2.5. The diurnal changes of OC, EC, and OC/EC ratios reflected prominent influences from local traffic patterns. Positive matrix factorization was performed using hourly data of PM_2.5 components (PMF_1-h), of which the results were justified by comparing to those using 23-h averaged data (PMF_23-h). Given that the secondary ion formation was still the dominant source (68.2%) of PM_2.5, and the average PM_2.5 concentration in urban Nanjing remained higher than Tier II limit (35 μg m^-3) of the Chinese National Ambient Air Quality Standard, controlling emissions of PM_2.5 precursor gases should be continued after the completion of Air Pollution Prevention and Control Action Plan in 2017.

Levels and sources of hourly PM2.5-related elements during the control period of the COVID-19 pandemic at a rural site between Beijing and Tianjin

To control the spread of the novel coronavirus disease 2019 (COVID-19) in China, many anthropogenic activities were reduced and even closed on the national scale. To study the impact of this reduction and closing down, hourly concentrations of PM2.5-related elements were measured at a rural site before (12-25 January 2020), during (26 January-9 February 2020) and after (22 March-2 April 2020) the control period when all people remained socially isolated in their homes and could not return to economic zones for work. Nine major sources were identified by the positive matrix factorization model, including fireworks burning, coal combustion, vehicle emissions, dust, Cr industry, oil combustion, Se industry, Zn smelter, and iron and steel industry. Before the control period, K, Fe, Ca, Zn, Ba and Cu were the main elements, and fireworks burning, Zn smelter and vehicle emissions provided the highest contributions to the total element mass with 55%, 12.1% and 10.3%, respectively. During the control period, K, Fe, Ba, Cu and Zn were the dominating elements, and fireworks burning and vehicle emissions contributed 55% and 27% of the total element mass. After the control period, Fe, K, Ca, Zn and Ba were the main elements, and dust and iron and steel industry were responsible for 56% and 21% of the total element mass. The increased contribution from vehicle emissions during the control period could be attributed to our sampling site being near a town hospital and the fact that the vehicle activities were not restricted. The source apportionment results were also related to air mass backward trajectories. The largest reductions of dust, coal combustion, and the industrial sources (Cr industry, Zn smelter, Se industry, iron and steel industry) were distinctly seen for northwest transport (Ulanqab) and were least significant for northeast transport (Tangshan and Tianjin).

Human exposure to air contaminants in sports environments

The aim of this review was to investigate human exposure to relevant indoor air contaminants, predictors affecting the levels, and the means to reduce the harmful exposure in indoor sports facilities. Our study revealed that the contaminants of primary concern are the following: particulate matter in indoor climbing, golf, and horse riding facilities; carbon dioxide and particulate matter in fitness centers, gymnasiums, and sports halls; Staphylococci on gymnasium surfaces; nitrogen dioxide and carbon monoxide in ice hockey arenas; carbon monoxide, nitrogen oxide(s), and particulate matter in motor sports arenas; and disinfection by-products in indoor chlorinated swimming pools. Means to reduce human exposure to indoor contaminants include the following: adequate mechanical ventilation with filters, suitable cleaning practices, a limited number of occupants in fitness centers and gymnasiums, the use of electric resurfacers instead of the engine powered resurfacers in ice hockey arenas, carefully regulated chlorine and temperature levels in indoor swimming pools, properly ventilated pools, and good personal hygiene. Because of the large number of susceptible people in these facilities, as well as all active people having an increased respiratory rate and airflow velocity, strict air quality requirements in indoor sports facilities should be maintained.

Understanding sources of fine particulate matter in China

Fine particulate matter has been a major concern in China as it is closely linked to issues such as haze, health and climate impacts. Since China released its new national air quality standard for fine particulate matter (PM2.5) in 2012, great efforts have been put into reducing its concentration and meeting the standard. Significant improvement has been seen in recent years, especially in Beijing, the capital city of China. This paper reviews how China understands its sources of fine particulate matter, the major contributor to haze, and the most recent findings by researchers. It covers the characteristics of PM2.5 in China, the major methods to understand its sources such as emission inventory and measurement networks, the major research programmes in air quality research, and the major measures that lead to successful control of fine particulate matter pollution. A great example of linking scientific findings to policy is the control of coal combustion from the residential sector in northern China. This review not only provides an overview of the fine particulate matter pollution problem in China, but also its experience of air quality management, which may benefit other countries facing similar issues. This article is part of a discussion meeting issue 'Air quality, past present and future'.

Contribution of Particulate Nitrate Photolysis to Heterogeneous Sulfate Formation for Winter Haze in China

Nitrate and sulfate are two key components of airborne particulate matter (PM). While multiple formation mechanisms have been proposed for sulfate, current air quality models commonly underestimate its concentrations and mass fractions during northern China winter haze events. On the other hand, current models usually overestimate the mass fractions of nitrate. Very recently, laboratory studies have proposed that nitrous acid (N(III)) produced by particulate nitrate photolysis can oxidize sulfur dioxide to produce sulfate. Here, for the first time, we parameterize this heterogeneous mechanism into the state-of-the-art Community Multi-scale Air Quality (CMAQ) model and quantify its contributions to sulfate formation. We find that the significance of this mechanism mainly depends on the enhancement effects (by 1-3 orders of magnitude as suggested by the available experimental studies) of nitrate photolysis rate constant (J NO 3 - ) in aerosol liquid water compared to that in the gas phase. Comparisons between model simulations and in-situ observations in Beijing suggest that this pathway can explain about 15% (assuming an enhancement factor (EF) of 10) to 65% (assuming EF = 100) of the model-observation gaps in sulfate concentrations during winter haze. Our study strongly calls for future research on reducing the uncertainty in EF.

Cause of PM2.5 pollution during the 2016-2017 heating season in Beijing, Tianjin, and Langfang, China

To investigate the cause of fine particulate matter (particles with an aerodynamic diameter less than 2.5 µm, PM_2.5) pollution in the heating season in the North China Plain (specifically Beijing, Tianjin, and Langfang), water-soluble ions and carbonaceous components in PM_2.5 were simultaneously measured by online instruments with 1-hr resolution, from November 15, 2016 to March 15, 2017. The results showed extreme severity of PM_2.5 pollution on a regional scale. Secondary inorganic ions (SNA, i.e., NO₃^-+SO₄²+ NH₄⁺) dominated the water-soluble ions, accounting for 30%-40% of PM_2.5, while the total carbon (TC, i.e., OC + EC) contributed to 26.5%-30.1% of PM_2.5 in the three cities. SNA were mainly responsible for the increasing PM_2.5 pollution compared with organic matter (OM). NO₃^- was the most abundant species among water-soluble ions, but SO₄^2- played a much more important role in driving the elevated PM_2.5 concentrations. The relative humidity (RH) and its precursor SO₂ were the key factors affecting the formation of sulfate. Homogeneous reactions dominated the formation of nitrate which was mainly limited by HNO₃ in ammonia-rich conditions. Secondary formation and regional transport from the heavily polluted region promoted the growth of PM_2.5 concentrations in the formation stage of PM_2.5 pollution in Beijing and Langfang. Regional transport or local emissions, along with secondary formation, made great contributions to the PM_2.5 pollution in the evolution stage of PM_2.5 pollution in Beijing and Langfang. The favourable meteorological conditions and regional transport from a relatively clean region both favored the diffusion of pollutants in all three cities.

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 (PM_2.5). 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.

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

The chemical species in PM_2.5 and air pollutant concentration data with 1-hr resolution were monitored synchronously between 15 November 2018 and 20 January 2019 in Linfen, China, which were analysed for multiple temporal patterns, and PM_2.5 source apportionment using positive matrix factorisation (PMF) modelling coupled with online chemical species data was conducted to obtain the apportionment results of distinct temporal patterns. The mean concentration of PM_2.5 was 124 μg/m³ during the heating period, and NO₃^- and organic carbon (OC) were the dominant species. The concentrations and percentages of NO₃^-, SO₄^2-, and OC increased notably during the growth periods of haze events, thereby indicating secondary particle formation. Six factors were identified by the PMF model during the heating period, including vehicular emissions (VE: 26.5%), secondary nitrate (SN: 16.5%), coal combustion and industrial emissions (CC&IE: 25.7%), secondary sulfate and secondary organic carbon (SS&SOC: 24.4%), biomass burning (BB: 1.0%), and crustal dust (CD: 5.9%). The primary sources of PM_2.5 on clean days were CD (33.3%), VE (23.1%), and SS&SOC (20.6%), while they were CC&IE (32.9%) and SS&SOC (28.3%) during the haze events. The contributions of secondary sources and CC&IE increased rapidly during the growth periods of haze events, while that of CD increased during the dissipation period. Diurnal variations in the contribution of secondary sources were mainly related to the accumulation and transformation of corresponding gaseous precursors. In comparison, contributions of CC&IE and VE varied as a function of the domestic heating load and peak levels occurred during the morning and evening rush hours. High contributions of major sources (CC&IE and SS&SOC) during haze events originated mainly from the north and south, while high contribution of a major source (CD) on clean days was from the northwest.

Significant Contribution of Primary Sources to Water-Soluble Organic Carbon During Spring in Beijing, China

Despite the significant role water-soluble organic carbon (WSOC) plays in climate and human health, sources and formation mechanisms of atmospheric WSOC are still unclear; especially in some heavily polluted areas. In this study, near real-time WSOC measurement was conducted in Beijing for the first time with a particle-into-liquid-sampler coupled to a total organic carbon analyzer during the springtime, together with collocated online measurements of other chemical components in fine particulate matter with a 1 h time resolution, including elemental carbon (EC), organic carbon (OC), multiple metals, and water-soluble ions. Good correlations of WSOC with primary OC, as well as carbon monoxide, indicated that major sources of WSOC were primary instead of secondary during the study period. The positive matrix factorization model-based source apportionment results quantified that 68 ± 19% of WSOC could be attributed to primary sources, with predominant contributions by biomass burning during the study period. This finding was further confirmed by the estimate with the modified EC-tracer method, suggesting significant contribution of primary sources to WSOC. However, the relative contribution of secondary source to WSOC increased during haze episodes. The WSOC/OC ratio exhibited similar diurnal distributions with O3 and correlated well with secondary WSOC, suggesting that the WSOC/OC ratio might act as an indicator of secondary formation when WSOC was dominated by primary sources. This study provided evidence that primary sources could be major sources of WSOC in some polluted megacities, such as Beijing. From this study, it can be seen that WSOC cannot be simply used as a surrogate of secondary organic aerosol, and its major sources could vary by season and location.

Airborne bacterial communities and antibiotic resistance gene dynamics in PM2.5 during rainfall

The biotoxicity and public health effects of airborne bacteria and antibiotic resistance genes (ARGs) in fine particulate matter (PM_2.5) are being increasingly recognized. The characteristics of bacterial community composition and ARGs in PM_2.5 under different rainfall conditions were studied based on the on-site synchronous measurements in downtown Beijing. Marked differences were evident in the bacterial community characteristics of PM_2.5 before, during, and after rain events (p < 0.05). The rain intensities affected the bacterial community abundance in PM_2.5 and heavy rain had greater washing effects. The Proteobacteria (phylum level), α-Proteobacteria (class level), Pseudomonadales (order level), Pseudomonadaceae (family level), and Cyanobacteria (genus level) were the dominant bacterial taxa associated with PM_2.5 in Beijing during rain events. However, the bacteria at each level that displayed the biggest percentage variance was not the dominant type under different rain intensities. The ermB, tetW, and mphE genes were the primary ARGs, with abundances of 18 to 30 copies/m³, which was a relatively smaller value than other observations. Real-time monitoring of the meteorological condition of rain events and physicochemical properties of PM_2.5 were used to identify the main factors during rainfall. The bacterial community was sensitive to the ionic and metal element components of PM_2.5 during rainfall. The abundance of ARGs was closely correlated with some groups of the bacterial community, which were also close to the initial value before the rain. Statistical analysis demonstrated that temperature, relative humidity, and duration of rain were the primary meteorological factors for the biological characteristics. The ionic species, rather than metal elements, in PM_2.5 were the sensitive factors for the bacteria community and ARGs, which varied at the phylum, class, order, family, and genus levels. The observations provide insights for the biological risk assessment in an urban rainfall water and the potential health impact on citizens.

Particulate air pollution and ischemic stroke hospitalization: How the associations vary by constituents in Shanghai, China

BACKGROUND

The identification of constituents of fine particulate matter (PM_2.5) air pollution that had key impacts of ischemic stroke (the predominant subtype of stroke) is important to understand the underlying biological mechanisms and develop air pollution control policies.

OBJECTIVES

To explore the associations between PM_2.5 constituents and hospitalization for ischemic stroke in Shanghai, China.

METHODS

We conducted a time-series study to explore the associations between 27 constituents of PM_2.5 and hospitalization for ischemic stroke in Shanghai, China from 2014 to 2016. The over-dispersed generalized additive models with adjustment for time, day of week, holidays, and weather conditions were used to estimate the associations. We also evaluated the robustness of the effect estimates for each constituent after adjusting for the confounding effects of PM_2.5 total mass and gaseous pollutants and the collinearity (the residual) between this constituent and PM_2.5 total mass. We also compared the associations between seasons.

RESULTS

In total, we identified 4186 ischemic stroke hospitalizations during the study period. The associations of ischemic stroke were consistently significant with elemental carbon and several elemental constituents (Chromium, Iron, Copper, Zinc, Arsenic, Selenium, and Lead) at lag 1 day in single-constituent models, models adjusting for PM_2.5 total mass or gaseous pollutants and models adjusting for collinearity. The associations were much stronger in cool season than in warm season.

CONCLUSIONS

The current study provides suggestive evidence that elemental carbon and some metallic elements may be mainly responsible for the risks of ischemic stroke hospitalization induced by short-term PM_2.5 exposure.

Characteristics of air pollutants inside and outside a primary school classroom in Beijing and respiratory health impact on children

This study investigated the spatial and temporal distributions of particulate and gaseous air pollutants in a primary school in Beijing and assessed their health impact on the children. The results show that air quality inside the classroom was greatly affected by the input of outdoor pollutants; high levels of pollution were observed during both the heating and nonheating periods and indicate that indoor and outdoor air pollution posed a threat to the children's health. Traffic sources near the primary school were the main contributors to indoor and outdoor pollutants during both periods. Moreover, air quality in this primary school was affected by coal combustion and atmospheric reactions during the heating and nonheating periods, respectively. Based on the estimation by exposure-response functions and the weighting of indoor and outdoor pollutants during different periods, the levels of PM_2.5, PM ₁₀ and O₃ at school had adverse respiratory health effects on children. Longer exposures during the nonheating period contributed to higher health risks. These results emphasized that emission sources nearby had a direct impact on air quality in school and children's respiratory health. Therefore, measures should be taken for double control on air pollution inside and outside the classroom to protect children from it.

The influence of spatial patterning on modeling PM2.5 constituents in Eastern Massachusetts

Geostatistical exposure methods for air pollution have inherent uncertainties, resulting in varying levels of exposure misclassification. In this study, we propose that areas representing clusters of PM_2.5 elements are potential predictor variables to be included in spatial models for particle composition. The inclusion of these clusters may minimize the exposure misclassification. We evaluated the influence of spatial patterning on modeling of 10 components of ambient PM_2.5, which included Al, Cu, Fe, K, Ni, Pb, S, Ti, V, and Zn. This study was performed in three stages. First, we applied a hybrid approach (combination of Empirical Bayesian Kriging and land use regression) to estimate spatial variability for each one of the 10 components of ambient PM_2.5. In this stage, we accounted for numerous predictors representing land use, transportation, demographic, and geographical characteristics. In the second stage, we applied the same hybrid approach adding clusters of each PM_2.5 component to the set of predictor variables. The clusters here were estimated by a multivariate clustering approach based on k means. Finally, in the last stage, we compared the estimates obtained from the model without clusters (first stage) and the model with clusters (second stage). Overall, our findings suggest significant influence of spatial clusters on modeling some PM_2.5 components. We observed that the clusters may affect the error of the prediction values and especially the proportion of explained variance for most of the PM_2.5 constituents evaluated in this study. The model with cluster presented a better performance for all PM_2.5 components, except for Pb, which the R² value decreased 8.51% when we included the clusters in the analysis; and for V, which the R² value did not change with the clusters. Models for Cu and Fe explained the highest concentration variance. The R² value for the model without cluster was 0.55 for both pollutants. When we accounted for clusters, R² value increased 13 and 7% for Cu (R² = 0.62) and Fe (R² = 0.59), respectively. The models for K and S presented the lowest performance for both models with and without cluster (although the model with cluster improved substantially the R² values). Better knowledge of the influence of spatial patterns on air pollution modeling should be of interest to policy makers to devise future strategies to improve human exposure assessment to air particulates while controlling for spatial patterns of ambient PM_2.5 elemental concentration.

Multivariate spatial patterns of ambient PM2.5 elemental concentrations in Eastern Massachusetts

Understanding the factors that affect spatial differences in PM_2.5 composition is crucial for implementing emissions control and health policies. Although previous studies have explored modeling of spatial patterns as a tool to improve human exposure assessment, little work has employed a multivariate clustering approach to identify spatial patterns in particle composition. In this study, we used this approach to assess the spatial patterns of ambient PM_2.5 elemental concentrations in Eastern Massachusetts in the United States. To distinguish one cluster of sites from another, we considered air pollution sources and geodemographic variables. We evaluated spatial patterns for 11 elemental components of ambient PM_2.5, which included S, K, Ca, Fe, Zn, Cu, Ti, Al, Pb, V, and Ni. The analyses for S, Ca, Cu, Ti, Al, and Pb resulted in: 2 clusters for Fe, Zn, V, and Ni; 3 clusters; and for 12 clusters for K. Overall, our findings suggest substantial variation of clusters among PM_2.5 components. In addition, land use, population density, and daily traffic were used as variables to more effectively characterize clusters of sites. We used R² values to estimate the effectiveness of each variable in characterizing clusters. Larger R² values indicate better the discrimination among the sites. For example, population density had the highest R² value when the analysis was performed for S, Ca, Zn, Ti, Al, Pb, and V; land use presented the highest R² value for Cu, V, and Ni; and, traffic showed the highest R² value for PM_2.5 mass concentration. This study improves the ability to model both the between- and within-area variability of source emissions and pollution regime, using concentrations of PM_2.5 components.

Evaluation of predictive capabilities of ordinary geostatistical interpolation, hybrid interpolation, and machine learning methods for estimating PM2.5 constituents over space

Numerous modeling approaches to estimate concentrations of PM_2.5 components have been developed to derive better exposures for health studies, including geostatistical interpolation approaches, land use regression models and, models based on remote sensing technology. Recently, there have been some efforts to develop models based on machine learning algorithms. Each one of these exposure assessment methods has inherent uncertainties resulting in varying levels of exposure misclassification. To date, only a few studies have attempted to systematically compare exposure estimates from different PM_2.5 constituent models. Our research addresses this gap, by comparing the predictive capabilities of ordinary geostatistical interpolation (Ordinary Kriging - OK), hybrid interpolation (combination of Empirical Bayesian Kriging and land use regression), and machine learning techniques (forest-based regression) for estimating PM_2.5 constituents in Eastern Massachusetts in the United States. We compared the estimates of 10 ambient PM_2.5 components, which included Al, Cu, Fe, K, Ni, Pb, S, Ti, V, and Zn. The OK model performed poorest for all PM_2.5 components, with an R² under 0.30. The hybrid model presented a slight improvement, especially for Cu and Fe, for which the R² value increased to 0.62 and 0.59, respectively. These elements presented the highest R² value from the hybrid model. The forest model presented the best performance, with R² values higher than 0.7 for most of the particle components, including Cu, Fe, Ni, Pb, Ti, and V. Same as observed with the hybrid model, the forest model for Cu and Fe explained the highest concentration variance, with a R² value equal to 0.88 and 0.92, respectively. The forest model for K, S, and Zn performed poorest with an R² value of 0.54, 0.37, and 0.44, respectively. The results presented here can be useful for the environmental health community to more accurately estimate PM_2.5 constituents over space.

Meteorological and chemical impacts on PM2.5 during a haze episode in a heavily polluted basin city of eastern China

Haze formation involves many interacting factors, such as secondary aerosol formation, unfavourable synoptic conditions and regional transport. The interaction between these factors complicates scientific understanding of the mechanism behind haze formation. In this study, we investigated the factors resulting in haze events in Longyou, a city located in a basin in China. Aerosol samples of PM_2.5 were collected for subsequent chemical composition analysis between 11 January and 5 February 2018. The impacts of wind on PM_2.5, SO₂ and NO₂ concentrations were analysed. Besides, the origin of air parcels and potential sources of PM_2.5 were analysed by backward trajectory, potential source contribution function (PSCF) and concentration-weighted trajectories (CWT). Among the water-soluble ions identified, NO₃^- had the highest concentration, with further analysis demonstrating the haze evolution was mainly driven by the reactions involving NO₃^- formation. The dramatic increase of nitrate is mainly due to the homogeneous reaction of nitric acid with ammonia, while sulfate is likely due to heterogeneous reactions of NO₂, SO₂ and NH₃. The average wind speed was less than 2 m/s during the aerosol sampling period, which could be considered as a stagnant state. Pollutants emitted by industrial area located in the northeast Longyou were probably brought to observation sites by continuous wind from northeast and accumulated gradually. Air parcels originating from the northeast of Zhejiang province also had large effects on haze pollution in Longyou. Together, our results showed that rapid secondary aerosol formation and unfavourable synoptic conditions are the main factors resulting in haze pollution in Longyou.