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In December 2019, the novel coronavirus pneumonia (COVID-19) occurred in Wuhan, Hubei Province, China. The epidemic quickly broke out and spread throughout the country. Now it becomes a pandemic that affects the whole world. In this study, three models were used to fit and predict the epidemic situation in China: a modified SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) dynamic model, a neural network method LSTM (Long Short-Term Memory), and a GWR (Geographically Weighted Regression) model reflecting spatial heterogeneity. Overall, all the three models performed well with great accuracy. The dynamic SEIRD prediction APE (absolute percent error) of China had been ≤ 1.0% since Mid-February. The LSTM model showed comparable accuracy. The GWR model took into account the influence of geographical differences, with R2 = 99.98% in fitting and 97.95% in prediction. Wilcoxon test showed that none of the three models outperformed the other two at the significance level of 0.05. The parametric analysis of the infectious rate and recovery rate demonstrated that China's national policies had effectively slowed down the spread of the epidemic. Furthermore, the models in this study provided a wide range of implications for other countries to predict the short-term and long-term trend of COVID-19, and to evaluate the intensity and effect of their interventions. [ABSTRACT FROM AUTHOR]

To investigate the effect of atmospheric particulates on the chemistry and acidity of rainwater during the washout process in areas with highly acidic rainfall, rainwater and particulates (before, during and after rain) were sampled and analyzed from June 2008 to June 2009 in the Yangtze River Delta region, China. The volume-weighted mean pH of the rainwater was only 4.44 in Hangzhou, and the average concentrations of TSP (total-suspended particulates) and PM10 (particulates with aerodynamic equivalent diameters of less than 10 μm) during the study period were 191 μg/m3 and 155 μg/m3, respectively. The measured acid buffering capacities of PM10 and TSP were 1.31 ± 0.45 and 1.61 ± 0.84 ml/mg, on average, respectively, indicating that both had a certain acid buffering capacity. Rain appeared to have a pronounced scouring effect on particulates, except for F− and Cl− in PM10. Upon combining the correlation analysis between ions in particulates and rainwater with Enrichment Factor and Principal Component Analysis of the rainwater chemistry, the results suggested that the components of rainwater were mainly derived from washout processes acting on particulates from crustal dust and building industry sources (Ca2+), marine sources (Na+, K+ (in spring), Mg2+ and Cl−) and anthropogenic sources, especially secondary aerosols emitted from agricultural land, motor vehicles and industrial plants (NO3−, SO42-, and NH4+). The F− in rainwater was mainly contributed by gaseous pollutants, such as HF from cooling systems, coal burning and surrounding factories. Implications: The interaction between particulates and rain in areas with highly acidic rainfall include the following: rain appears to have a pronounced scouring effect on most components in the particulates; the components of the rainwater are mainly derived from washout processes acting on PM contributed by crustal dust, sea salt and secondary aerosol subcloud; and the acid buffering capacities of PM10 and TSP in Hangzhou are 1.31 ± 0.45 and 1.61 ± 0.84 ml/mg, respectively, and had a certain acid buffering capacity toward rainwater. [ABSTRACT FROM AUTHOR]

Based on published literature and typical profiles from the Nankai University source library, a total of 3326 chemical profiles of the main primary sources of ambient particulate matter (PM) across China from 1987 to 2017 are investigated and reviewed to trace the evolution of their main components and identify the main influencing factors concerning their evolution. In general, the source chemical profiles are varied with respect to their sources and are influenced by different sampling methods. The most complicated profiles are likely attributed to coal combustion (CC) and industrial emissions (IE). The profiles of vehicle emissions (VE) are dominated by organic carbon (OC) and elemental carbon (EC), and vary due to the changing standards of sulfur and additives in gasoline and diesel as well as the sampling methods used. In addition to the sampling methods used, the profiles of biomass burning (BB) and cooking emissions (CE) are also impacted by the different biofuel categories and cooking types, respectively. The variations of the chemical profiles of different sources, and the homogeneity of the subtype source profiles within the same source category are examined using uncertainty analysis and cluster analysis. As a result, a relatively large variation is found in the source profiles of CC, VE, IE, and BB, indicating that these sources urgently require the establishment of local profiles due to their high uncertainties. The results presented highlight the need for further investigation of more specific markers (e.g., isotopes, organic compounds, and gaseous precursors), in addition to routinely measured components, in order to properly discriminate sources. Although the chemical profiles of the main sources have been previously reported in the literature, it should be noted that some of these chemical profiles are currently out of date and need to be updated immediately. Additionally, in the future, specific focus should be placed on the source profile subtypes, especially with respect to local IE in China. [ABSTRACT FROM AUTHOR]

Abstract To identify the critical factors impacting the number concentration of particles with the aerodynamic diameters less than 2.5 μm (PNC 2.5), the continuous measurement of PNC 2.5 , chemical components in PM 2.5 , gaseous pollutants and meteorological conditions were conducted at an urban site in Tianjin in June 2015. Results indicated that the average PNC 2.5 was 2839 ± 2430 d N /dlog D p 1/cm3 during the campaign. Compared to other meteorological parameters, the relative humidity (RH) had the strongest relationship with PNC 2.5 , with a Pearson's correlation coefficient of 0.53, and RH larger than 30% influenced strongly PNC 2.5. The important influence of secondary reactions on PNC 2.5 was inferred due to higher correlation coefficients between PNC 2.5 and SO 4 2 −, NO 3 −, NH 4 + (r = 0.78–0.89; p < 0.01) and between PNC 2.5 and ratios that represent the conversion of nitrogen and sulfur oxides to particulate matter (r = 0.42–0.49; p < 0.01). Under specific RH conditions, there were even stronger correlations between PNC 2.5 and NO 3 −, SO 4 2 −, NH 4 +, while those between PNC 2.5 and EC, OC were relatively weak, especially when RH exceeded 50%. Principal component analysis (PCA) and Pearson's correlation analysis indicated that secondary sources, vehicle emission and coal combustion might be major contributors to PNC 2.5. Backward trajectory and potential source contribution function (PSCF) analysis suggested that the transport of air masses originated from these regions around Tianjin (Liaoning, Hebei, Shandong and Jiangsu) influenced critically PNC 2.5. The north of Jiangsu, the west of Shandong, and the east of Hebei were distinguished as major potential source-areas of PNC 2.5 by PSCF model. Graphical abstract Unlabelled Image [ABSTRACT FROM AUTHOR]

A long-term (2013–2019) PM 2.5 speciation dataset measured in Tianjin, the largest industrial city in northern China, was analyzed with dispersion normalized positive matrix factorization (DN-PMF). The trends of source apportioned PM 2.5 were used to assess the effectiveness of source-specific control policies and measures in support of the two China's Clean Air Actions implemented nationwide in 2013–2017 and 2018–2020, respectively. Eight sources were resolved from the DN-PMF analysis: coal combustion (CC), biomass burning (BB), vehicular emissions, dust, steelmaking and galvanizing emissions, a mixed sulfate-rich factor and secondary nitrate. After adjustment for meteorological fluctuations, a substantial improvement in PM 2.5 air quality was observed in Tianjin with decreases in PM 2.5 at an annual rate of 6.6%/y. PM 2.5 from CC decreased by 4.1%/y. The reductions in SO 2 concentration, PM 2.5 contributed by CC, and sulfate demonstrated the improved control of CC-related emissions and fuel quality. Policies aimed at eliminating winter-heating pollution have had substantial success as shown by reduced heating-related SO 2 , CC, and sulfate from 2013 to 2019. The two industrial source types showed sharp drops after the 2013 mandated controls went into effect to phaseout outdated iron/steel production and enforce tighter emission standards for these industries. BB reduced significantly by 2016 and remained low due to the no open field burning policy. Vehicular emissions and road/soil dust declined over the Action's first phase followed by positive upward trends, showing that further emission controls are needed. Nitrate concentrations remained constant although NO X emissions dropped significantly. The lack of a decrease in nitrate may result from increased ammonia emissions from enhanced vehicular NO X controls. The port and shipping emissions were evident implying their impacts on coastal air quality. These results affirm the effectiveness of the Clean Air Actions in reducing primary anthropogenic emissions. However, further emission reductions are needed to meet global health-based air quality standards. [Display omitted] • Trends of source apportioned PM 2.5 were analyzed with 1894 filter samples. • Reductions in primary anthropogenic emissions notably lowered PM 2.5 concentration. • Nitrate remained constant although NO X emissions dropped significantly. • Port and shipping emissions were evident in Tianjin. [ABSTRACT FROM AUTHOR]

Heze city, a medium-size city in Shandong province, Eastern China. Ambient PM 2.5 samples were collected in urban area of Heze from August 2015 to April 2016, and chemical species and sources of PM 2.5 were investigated in this paper. The results indicated that the average concentration of PM 2.5 was 100.9 μg/m 3 during the sampling period, and the water-soluble ions, carbonaceous species included elemental carbon (EC) and organic carbon (OC), as well as elements contributed 32.7–51.7%, 16.3% and 12.5%, respectively, to PM 2.5 . Pearson's correlation analysis showed that the existing form of NH 4 + was more complex and diverse in spring/summer, and ammonium nitrate, ammonium sulfate and ammonium hydrogen sulfate might be major form of NH 4 + in autumn/winter. Correlation analysis between PM 2.5 and SO 4 2− /NO 3 − , PM 2.5 and OC/EC during different seasons suggested that mobile sources might make an important impact on the increase of PM 2.5 concentrations in spring/summer, and stationary sources might play a critical role on the increase of PM 2.5 concentrations in autumn/winter. Seven factors were selected in Positive Matrix Factorization (PMF) models analysis based on the Error Estimation (EE) diagnostics during different seasons. Secondary source had the highest contribution to PM 2.5 in Heze for the whole year, and followed by coal combustion, vehicle exhaust, soil dust, construction dust, biomass burning and metal manufacturing, and their annual contributions to PM 2.5 were 26.5%, 17.2%, 16.5%, 11.5%, 7.7%, 7.0% and 3.8%, respectively. The air masses that were originated from Mongolia reflected the features of large-scale and long-distance air transport; while the air masses that began in Jiangsu, Shandong and Henan showed the features of small-scale and short-distance. Shandong, Henan and Jiangsu were identified as the major potential sources-areas of PM 2.5 by using potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) models. [ABSTRACT FROM AUTHOR]

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

Ambient PM10 was sampled in six northern China cities (Urumqi, Yinchuan, Taiyuan, Anyang, Tianjin and Jinan) from December 1999 to July 2002, and analyzed for 16 chemical elements, two water-soluble ions, total carbon, and organic carbon. In addition, chemical source profiles consisting of the same particulate components were obtained from a number of naturally occurring geological sources (soil dust from exposed lands) and sources of atmospheric particulates resulting from human activities (resuspended dust, cement, coal combustion fly ash, vehicle exhaust, and secondary particles). Ambient and source data were used in a chemical mass balance (CMB) receptor model to determine the major source of PM10 in these six cities. Results of CMB modeling showed that the major source of ambient PM10 in all the cities was resuspended dust. Significant contributions from coal fly ash were also found in all six cities. [Copyright &y& Elsevier]

Potential health benefits from improved ambient air quality during the COVID-19 shutdown have been recently reported and discussed. Despite the shutdown measures being in place, northern China still suffered severe haze episodes (HE) that are not yet fully understood, particularly how the source emissions changed. Thus, the meteorological conditions and source emissions in processing five HEs occurred in Beijing-Tianjin-Hebei area were investigated by analyzing a comprehensive real-time measurement dataset including air quality data, particle physics, optical properties, chemistry, aerosol lidar remote sensing, and meteorology. Three HEs recorded before the shutdown began were related to accumulated primary pollutants and secondary aerosol formation under unfavorable dispersion conditions. The common "business as usual" emissions from local primary sources in this highly polluted area exceeded the wintertime atmospheric diffusive capacity to disperse them. Thus, an intensive haze formed under these adverse meteorological conditions such as in the first HE, with coal combustion to be the predominant source. Positive responses to the shutdown measures were demonstrated by reduced contributions from traffic and dust during the final two HEs that overlapped the Spring and Lantern Festivals, respectively. Local meteorological dispersion during the Spring Festival was the poorest among the five HEs. Increased residential burning plus fireworks emissions contributed to the elevated PM 2.5 with the potential of enhancing the HEs. Our results highlight that reductions from shutdown measures alone do not prevent the occurrence of HEs. To further reduce air pollution and thus improve public health, abatement strategies with an emphasis on residential burning are needed. [Display omitted] • Even with increased emissions controls, high pollution events occur in China. • "Business as usual" emissions in Tianjin exceeded winter diffusive capacity. • COVID shutdown reductions were offset by residential burning and fireworks. • Coal combustion remains a major source of PM 2.5. Fireworks plus residential burning contributed to the elevated PM 2.5 with the potential of enhancing hazes. [ABSTRACT FROM AUTHOR]

• Three typical crop straws of corn, wheat and rice in northern China were carried in the open burning experiment • A novel open combustion simulation device was employed • Particle number concentrations and mass concentrations were determined in the size ranging of 0.006~9.890 µm • Size distribution characteristics of various chemical components were analyzed and discussed. • Source chemical profiles were constructed for PM 0.1 , PM 1 , PM 2.5 , and PM 10 Crop residue open burning is an important emission source of ambient particles in China. This study analyzed the particle emission characteristics of crop residue open burning through combustion experiments with a novel open combustion simulation device using three typical crop straws in north China (corn, wheat, and rice). Particle samples size ranging from 0.006–9.890 µm were collected by an Electrical Low Pressure Impactor plus, a high size-resolution instrument capable of dividing particles into 14 size stages. The size distributions of organic carbon (OC), elemental carbon (EC), water-soluble ions, and elements were analyzed, and source chemical profiles were constructed for PM 0.1 , PM 1 , PM 2.5 , and PM 10. The number concentration of particles was concentrated in the Aiken nuclei mode (0.006–0.054 µm), accounting for 75% of the total number, whereas the mass concentration was concentrated in the accumulation mode (0.054–0.949 µm), accounting for 85.43% of the mass loading. OC, EC, Cl−, and K(include total K and water-soluble K) were the major chemical components of the particles, whose mass percentage distributions differed from those of other components. These five main components exhibited a bell-shaped size distribution in the 0.006–9.890 µm range, whereas the other components exhibited a U-shaped distribution. Among the chemical profiles for PM 0.1 –PM 10 , OC was the most important component at 10–30%, followed by EC at 2%–8%. The proportions of K+, Cl−, and K varied substantially in different experimental groups, ranging from 0–15%, and K+ and Cl− were significantly correlated (r = 0.878, α = 0.000). [Display omitted] [ABSTRACT FROM AUTHOR]

This study addresses the ongoing debate concerning the origin of ambient sulfate in northern China, and provides more evidence for primary sulfate from the perspective of source emission. An extensive field experiment was conducted, involving the collection of size-segregated particulate matter emitted under various coal types and combustion conditions for subsequent analysis. Our findings reveal that residential coal combustion (RCC) emits significant amounts of primary sulfate, constituting approximately 26.48 ± 15.43% of the particulate mass. Moreover, these emissions exhibit a strong association with NH 4 +, leading to the formation of stable ammonium sulfate compounds. The emission of sulfate is intricately linked to both coal quality and combustion conditions. Notably, the oversight of δ34S isotope enrichment in RCC particulate matter, which distinctly differs from the raw coal, introduces uncertainties in the quantitative assessment of sulfate sources and formation pathways. Therefore, more in-depth explorations are necessary to elucidate the precise processes involved in RCC sulfate transformation and to accurately determine its environmental impact. • Increase knowledge of RCC sulfate emission characteristics and influences. • Demonstrate RCC emits stable primary sulfate from multiple evidences. [ABSTRACT FROM AUTHOR]

The emission characteristics of biomass combustion in rural household stoves were measured in northern China. The Electrical Low-Pressure Impactor plus (ELPI+) was used to collect the emitted particles in the size range of 0.006–9.890 μm. The particle number concentration (PNC), particle mass concentration (PMC), and size distribution characteristics of organic carbon (OC), elemental carbon (EC), and water-soluble ions were analyzed. The experimental results showed that the PNC peaked at 0.006–0.030 μm, and the PMC peaked at 0.257–0.603 μm. The mass concentration of OC and EC exhibited a "bell-shaped" size distribution in the 0–10 μm range. The OC content of particles reached its peak at 0.006–0.016 μm and ranged from 25% to 38%. The mass concentration and percentage of K+ and Cl− showed "bell-shaped" size distribution characteristics and the contents of K+ and Cl− in each particle size group were significantly correlated. The chemical source profiles of PM 0.1 , PM 1 , PM 2.5 , and PM 10 were constructed, whose main components were OC, EC, K+, and Cl−. PM 2.5 profile included 8.39–20.27% OC, 1.94–20.96% EC, 0.25–13.36% K+, and 0.36–16.97% Cl−. Unlabelled Image • Field measurement was conducted using typical rural stoves in north China. • Common household fuels of corn, wheat, rice straws and fuel wood were selected. • Particles and mass concentrations were determined in the size range 0.006–9.890 μm. • Analyzed size distribution characteristics of carbonaceous and water-soluble ions. • Source chemical profiles are constructed for PM 0.1 , PM 1 , PM 2.5 , and PM 10. [ABSTRACT FROM AUTHOR]

From 1st June to 31st August 2015, the online datasets (the water soluble inorganic ions (WSIIs), OC and EC in PM 2.5 , and SO 2 , NO 2 , NO) were measured continuously at Tianjin. Source apportionment of atmospheric pollutants was carried out by using PMF and ME2 models based on the online datasets. During summer in Tianjin, the ammonium sulfate/ammonium hydrogen sulfate might be major forms of sulfate in the atmospheric aerosol, while the ammonium nitrate might be major forms of nitrate. The poor correlation between OC and EC might be caused by the changes of emission sources and the production of secondary organic carbon (SOC). Five source-categories that contributed to atmospheric pollutants were extracted by PMF and ME2 models, respectively. The profiles calculated by PMF and ME2 models were consistent, and the source contributions estimated by the two models were also similar. The correlations (R 2 = 0.84–0.94) were better on the time series of the contributed concentrations for the same source-category calculated from PMF and ME2 models. The source-categories were identified as secondary sources (the contribution of 25.4–26.1%), vehicle exhaust (23.3–25.4%), coal combustion (16.5–18.2%), crustal dust (13.2–14.0%) and biomass burning (9.1–10.2%). For the same source-category identified from PMF and ME2 models, the differences of profiles might be attributed to the differences of calculated methods from the two models and the uncertainties of the online datasets. [ABSTRACT FROM AUTHOR]

Ambient PM 2.5 samples were collected in the urban area of Taian in China in August–September and November, 2014. The chemical compositions and emission sources of PM 2.5 were analyzed. The results indicated that the mean concentration of PM 2.5 reached 70.8 μg/m 3 during the non-heating period, and water soluble inorganic ions (WSIIs), carbonaceous materials, including elemental carbon (EC) and organic carbon (OC); and elements contributed 43.80%, 10.34% and 17.36%, respectively, to PM 2.5 . The mean concentrations of WSIIs at three sampling sites decreased in the same order: SO 4 2 − > NH 4 + > NO 3 − > Cl − during the non-heating period. NO 3 − and NH 4 + , SO 4 2 − and NH 4 + , showed extremely significant positive-correlations ( r = 0.79, 0.54; P < 0.01). The variability of OC was larger than the variability of EC during the non-heating period. The high concentration of secondary organic carbon (SOC) could reduce correlation-level between the OC and EC. Moreover, the percentages and concentrations of the total detected elements (TDE) increased significantly, ranging from August–September to November ( P < 0.01). Major sources of PM 2.5 identified from positive matrix factorization (PMF) model and enrichment factors (EFs) included secondary aerosol, coal combustion, metal manufacturing, soil dust/resuspended dust/construction dust and vehicle exhaust/biomass burning, which contributed 27.47%, 17.94%, 19.06%, 9.41% and 16.65%, respectively, to PM 2.5 . The backward trajectory analysis identified three transport pathways that originated from Mongolia (12% of the total trajectories), Inner Mongolia (2%), and southeast of Shandong Province (86%), and the potential source contribution function (PSCF) model identified southeast of Shandong Province was mainly a potential source-area that affected air quality in Taian. [ABSTRACT FROM AUTHOR]

Refined source apportionment plays an important role in precise control of atmospheric particulate matter sources. PM 2.5 samples were collected and analysed in Tian'jin from February to October 2016, and directional contribution of individual source category was apportioned by applying a new approach. Results indicated that the mean concentration of PM 2.5 was 64 μg/m3 during the studying period. NO 3 − and OC were dominated species in PM 2.5 mass. The concentrations of OC and Cl−, and OC/EC were higher in winter than other seasons. Secondary sources (SS), coal and biomass burning (CBB), vehicle exhaust (VE), crustal dust (CD), and industrial emission (IE), were identified as major sources using the PMF model, their contributions to PM 2.5 were 28.6–38.1%, 17.3–20.1%, 21.1–23.5%, 10.9–14.2%, and 4.0–10.3%, respectively. The contributions of dust source and coal combustion in Tian'jin are decreasing, while that of motor vehicles is gradually highlighted. The contribution of secondary sources is maintained at a high level in recent years. Source contributions from the directions 1–3 (SW, NW, NE) for SS (16.8%, 8.5%, 4.0%), CBB (10.1%, 8.5%, 2.0%), VE (8.7%, 7.9%, 3.2%), CD (7.3%, 7.0%, 0.9%), and IE (4.2%, 3.8%, 1.4%), were observed at TG site. Source contributions from the directions1–4 (SW, WNW, NNW, NE) for SS (16.5%, 9.7%, 5.9%, 6.3%), CBB (8.0%, 5.0%, 2.5%, 1.9%), VE (8.5%, 6.8%, 3.5%, 3.3%), CD (5.9%, 3.2%, 2.0%, 1.0%), IE (2.0%, 0.9%, 0.6%, 0.6%), were observed at FK site. The contributions of major sources from SW direction were higher than other directions. • Sources contributions of ambient PM 2.5 from directional directions was apportioned. • Contributions of dust source and coal combustion in Tian'jin are decreasing, while that of vehicles is gradually highlighted. • Contributions of major sources from south-west direction are high at two studying sites. [ABSTRACT FROM AUTHOR]

Air pollution over the oceans has received less attention compared to densely populated urban areas of continents. The Bohai Sea, a semi-enclosed sea in northern China, is surrounded by thirteen industrial cities that have experienced significant improvements in air quality over the past decade. However, the changes in air pollution over the Bohai Sea and its impacts on surrounding cities remain poorly understood. To address this, this study investigated the evolution of air pollution and its chemical composition in the Bohai Sea over four decades, utilizing satellite remote sensing data, reanalysis datasets, emissions inventories, and statistical modeling. Historically, the region has suffered from severe air pollution, resulting from a combination of continental emissions and marine inputs (e.g., sea salt, ports and maritime vessel activities). The aerosol optical depth (AOD) over the sea was higher than the mean levels observed in its surrounding coastal cities. Statistically, 45% of the air masses reaching the Bohai Sea are associated with natural sources (dust- and marine-rich), while the remainder carry anthropogenic pollutants from continental regions. With the exception of Cangzhou city, these coastal cities suffer from air pollutants originating from the Bohai Sea. Cities in the northern region of the sea, spanning from Tianjin to Yingkou, are particularly impacted. The majority of the surrounding cities are affected by a large proportion of anthropogenic aerosol types transported through air masses from the Bohai Sea, including those from biomass burning and industrial activities. These findings emphasize the considerable influence of human-induced sources in the Bohai Sea on neighboring urban areas. Furthermore, being a maritime region, natural sources like sea salt and dust from the sea may also exert a discernible impact on the neighboring environment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

A long-term (2013-2019) PM 2.5 speciation dataset measured in Tianjin, the largest industrial city in northern China, was analyzed with dispersion normalized positive matrix factorization (DN-PMF). The trends of source apportioned PM 2.5 were used to assess the effectiveness of source-specific control policies and measures in support of the two China's Clean Air Actions implemented nationwide in 2013-2017 and 2018-2020, respectively. Eight sources were resolved from the DN-PMF analysis: coal combustion (CC), biomass burning (BB), vehicular emissions, dust, steelmaking and galvanizing emissions, a mixed sulfate-rich factor and secondary nitrate. After adjustment for meteorological fluctuations, a substantial improvement in PM 2.5 air quality was observed in Tianjin with decreases in PM 2.5 at an annual rate of 6.6%/y. PM 2.5 from CC decreased by 4.1%/y. The reductions in SO 2 concentration, PM 2.5 contributed by CC, and sulfate demonstrated the improved control of CC-related emissions and fuel quality. Policies aimed at eliminating winter-heating pollution have had substantial success as shown by reduced heating-related SO 2 , CC, and sulfate from 2013 to 2019. The two industrial source types showed sharp drops after the 2013 mandated controls went into effect to phaseout outdated iron/steel production and enforce tighter emission standards for these industries. BB reduced significantly by 2016 and remained low due to the no open field burning policy. Vehicular emissions and road/soil dust declined over the Action's first phase followed by positive upward trends, showing that further emission controls are needed. Nitrate concentrations remained constant although NO X emissions dropped significantly. The lack of a decrease in nitrate may result from increased ammonia emissions from enhanced vehicular NO X controls. The port and shipping emissions were evident implying their impacts on coastal air quality. These results affirm the effectiveness of the Clean Air Actions in reducing primary anthropogenic emissions. However, further emission reductions are needed to meet global health-based air quality standards., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Particulate nitrate plays an increasingly important role in the formation of air pollution process, while the main mechanisms of nitrate concentration change are different in each stage, same as the driving factors. In this study, we proposed an episode-based analysis to illustrate the typical nitrate evolution patterns and identify the possible impacting factors in different evolution stages. Applying into twelve air pollution episodes, three typical patterns of nitrate evolution were abstracted, and the corresponding conceptual models were constructed. All the pollution episodes were grouped by their evolving shapes, which were driven by physical and chemical processes. Episodes started slowly typically arose from gradual pollutant accumulation, both locally and regionally, and chemical formation under high humidity. Type 1 ("hump-shaped type"), accounting for 66.3 % of the total episode durations, including two "peak" concentrations, displays a rapid growth rate which could up to 4.6 μg m -3 h -1 in average, mainly relying on the sharp drop in the planetary boundary layer height. Short scavenging processes and thoroughly dissipated stages of the pollution episodes always accompanied by strong north wind affected by Siberia-Mongolia cold current. Type 2 ("triangle-shaped type", 24.3 %) shows a gentle growth rate and short duration. Compared with Type 1, chemical process may be more important "source" for the increase of nitrate concentration during Type 2. Type 3 ("trapezoid-shaped type", 9.4 %) presents a long platform stage, during which high humidity (RH > 90 %) provides favorable conditions for wet removal and secondary production, and the updraft can carry pollutants to high altitude. The source and sink are roughly balanced for Type 3. Our study highlights the importance of pattern identification for understanding the nitrate evolution behavior, it may also provide insights for pollution prediction and scientific mitigation strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The role of chloride in atmospheric chemistry received increased attention over recent years. Given the primary and chemical-active nature of PM 2.5 -bound chlorine (p-Cl - ), it makes sense to get to know the sources and processes of p-Cl - . The temporal behavior of observed p-Cl - concentration based on 1-h high resolution exhibited seasonal variation of high in winter, low in summer and diurnal variation of high in the morning, low in afternoon. Meteorological normalization technique based on random forest was used to disentangle the effects of emission changes which affected the seasonal variation and meteorology which was related to diurnal variation on p-Cl - . Generalized additive model (GAM) identified RH and temperature as the key meteorological factors of p-Cl - generation, and p-Cl - pollution was serious under the condition of low temperature and high RH. Dispersion-normalized positive matrix factorization (DN-PMF) was used to apportion the p-Cl - to its sources, finding that coal combustion was the main source of p-Cl - , followed by biomass burning and industrial process emissions. Our results will provide the basis for further analysis the causes of p-Cl - pollution and composite air pollution control strategies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Sand and dust storms (SDS) frequently hit northern China and adversely impact both environment and health. The carbonaceous components, inorganic elements, water-soluble ions, and meteorological parameters of several severe SDS episodes have been measured in a supersite in Tianjin, which is a big and representative city located in SDS transmission pathway in northern China. Six SDS episodes were identified in Spring, 2021. The maximum PM 10 mass concentration was 2684 and 1664 μg/m 3 in SDS1 and SDS3, respectively. North and northwest wind was dominant and significant differences were found in wind speed and RH between the SDS and non-SDS episodes. North dust from Inner Mongolia and Mongolia was determined by back trajectory analysis as the probable source region. The mass concentration of SO 4 2- , NO 3 - , and NH 4 + decreased in PM 2.5 . Increase of Na + indicate dust source for short length SDS transmission in northern China. The ratio of elements could also be used to distinguish SDS and non-SDS episodes identify north and northwest source for the SDS episodes. Pb/Al, Zn/Al, and Si/Al could be regarded as indicators for SDS and non-SDS episodes, Ca/Al and Ca/Si can help to indicate SDS source direction. This study provides a variety of evidences for the dust source identification and reveals the characteristics of the most severe SDS episodes of the decade in Tianjin during Spring 2021.+ and low SO 4 2- SDS / SO 4 2- non-SDS indicate dust source for short length SDS transmission in northern China. The ratio of elements could also be used to distinguish SDS and non-SDS episodes identify north and northwest source for the SDS episodes. Pb/Al, Zn/Al, and Si/Al could be regarded as indicators for SDS and non-SDS episodes, Ca/Al and Ca/Si can help to indicate SDS source direction. This study provides a variety of evidences for the dust source identification and reveals the characteristics of the most severe SDS episodes of the decade in Tianjin during Spring 2021., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The development of a refined fugitive dust emission inventory is vital for prevention and control of air pollution. In this study, a fugitive dust emission inventory of soil dust (SD), road dust (RD), and construction dust (CD) in Xiong'an New Area (XANA) for 2020 was developed by collecting activity data and combining remote sensing and field investigation data based on a popular compilation technology in China. The CALPUFF model was used to elucidate the contribution characteristics of dust sources to ambient particulate matter (PM), and the accuracy of the dust emission inventory compilation method was verified. The results show that the total emissions of PM 10 and PM 2.5 were 43,081.14 tons and 9701.69 tons, respectively. Meanwhile, RD and CD were the main emission sources, accounting for over 98.49% of the total emissions. The total contribution from the different types of dust sources to the ambient PM 10 was 42.59 μg/m 3 (29.38%), with the contribution of RD (32.63 μg/m 3 , 22.51%) being approximately three times that of CD (9.78 μg/m 3 , 6.74%). Roads were the main source of fugitive dust, but large-scale infrastructure construction was the main cause of the high emission and high contribution of RD. The results show that the emission inventory compilation method can be used to estimate the emissions of dust sources, while the method used to calculate the emission of SD may be more suitable for dry and semi-dry areas with less rainfall. It was also found that when the dust emissions stay stable, the contribution of dust sources to the ambient PM 10 in different seasons can vary by 3-4 times. Therefore, under adverse meteorological conditions, it is necessary to strengthen the control of various dust sources and reduce the influence of human factors on them., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Based on the wind erosion equation and the use of moderate resolution imaging spectroradiometer (MODIS) satellite remote sensing data combined with parameter normalization processing, an optimized high spatial-temporal resolution soil fugitive dust (SFD) emission inventory compiling method was proposed in this study. The "2 + 26" cities in northern China, where heavy pollution frequently occurs, were used as a case study. Using the optimized method, we estimated that the PM 50 , PM 10 , and PM 2.5 emissions from SFD of "2 + 26" cities in 2018 were 2,014,927, 1,007,463, and 151,120 tons, respectively. The dust emissions and emission factors of each city presented significant differences and were generally of a greater level in high-latitude areas (such as cities in Hebei Province) than in low-latitude areas (such as cities in Henan and Shandong Province). Moreover, with an increase in latitude, vegetation cover factors generally exhibit an upward trend, while temperature and rainfall exhibit a downward trend. The dust emissions in the different months showed significant differences. The total dust emission reached the highest level in "late winter-early spring" season (February to April), and the monthly emission accounted for 15-17% of the annual emissions. While in the "summer-autumn" season (July to November), it is the lowest level of the whole year, monthly emissions accounted for 3-5% of the annual emissions. The emission inventory method proposed in this study can provide a reference for dust emission assessment and further pollution prevention and control work., (Copyright © 2021. Published by Elsevier Ltd.)

Levels of toxic elements in ambient PM 2.5 were measured from 29 October 2019 to 30 March 2020 in Linfen, China, to assess the health risks they posed and to identify critical risk sources during different periods of the COVID-19 lockdown and haze episodes using positive matrix factorization (PMF) and a health-risk assessment model. The mean PM 2.5 concentration during the study period was 145 μg/m 3 , and the 10 investigated toxic elements accounted for 0.31% of the PM 2.5 mass. The total non-cancer risk (HI) and total cancer risk (TCR) of the selected toxic elements exceed the US EPA limits for children and adults. The HI for children was 2.3 times that for adults for all periods, which is likely due to the high inhalation rate per unit body weight for children. While the TCR for adults was 1.7 times that of children, which is mainly attributed to potential longer exposure duration for adults. The HI and TCR of the toxic elements during full lockdown were reduced by 66% and 58%, respectively, compared to their pre-lockdown levels. The HI and TCR were primarily attributable to Mn and As, respectively. Health risks during haze episodes were significantly higher than the average levels during COVID-19 lockdowns, though the HI and TCR of the selected toxic elements during full-lockdown haze episodes were 68% and 17% lower, respectively, than were the levels during pre-lockdown haze episodes. During the study period, fugitive dust and steel-related smelting were the highest contributors to HI and TCR, respectively, and decreased in these emission sources contributed the most to the lower health risks observed during the full lockdown. There, the control of these sources is critical to effectively reduce public health risks., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

PM 2.5 in Shijiazhuang was collected from October 15, 2018 to January 31, 2019, and selected toxic elements were measured. Five typical haze episodes were chosen to analyze the health risks and critical risk sources. Toxic elements during the haze episodes accounted for 0.33% of PM 2.5 mass. Non-cancer risk of toxic elements for children was 1.8 times higher than that for adults during the haze episodes, while cancer risk for adults was 2.5 times higher than that for children; cancer and non-cancer risks were primarily attributable to As and Mn, respectively. Health risks of toxic elements increased during the growth and stable periods of haze episodes. Non-cancer and cancer risks of toxic elements during the haze stable periods were higher than other haze stages, and higher for children than for adults during the stable period. Mn was the largest contributor to non-cancer risk during different haze stages, while As was the largest contributor to cancer risk. Crustal dust, vehicle emissions, and industrial emissions were critical sources of cancer risk during the clean-air periods; while vehicle emissions, coal combustion, and crustal dust were key sources of cancer risk during the haze episodes. Cancer risks of crustal dust and vehicle emissions during the haze episodes were 2.0 and 1.7 times higher than those in the clean-air periods. Non-cancer risks from emission sources were not found during different periods. Cancer risks of biomass burning and coal combustion increased rapidly during the haze growth period, while that of coal combustion decreased sharply during the dissipation period. Vehicle emissions, crustal dust, and coal combustion were significant cancer risk sources during different haze stages, cancer risk of each source was the highest during the stable period. Southern Hebei, Northern and central Shaanxi were potential risk regions that affected the health of both adults and children in Shijiazhuang., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Factor analysis models use the covariance of measured variables to identify and apportion sources. These models, particularly positive matrix factorization (PMF), have been extensively used for analyzing particle number concentrations (PNCs) datasets. However, the variation of observed PNCs and particle size distribution are driven by both the source emission rates and atmospheric dispersion as well as chemical and physical transformation processes. This variation in the observation data caused by meteorologically induced dilution reduces the ability to obtain accurate source apportionment results. To reduce the influence of dilution on quantitative source estimates, a methodology for improving the accuracy of source apportionment results by incorporating a measure of dispersion, the ventilation coefficient, into the PMF analysis (called dispersion normalized PMF, DN-PMF) was applied to a PNC dataset measured from a field campaign that includes the Spring Festival event and the start of the COVID-19 lockdown in Tianjin, China. The data also included gaseous pollutants and hourly PM 2.5 compositional data. Eight factors were resolved and interpreted as municipal incinerator, traffic nucleation, secondary inorganic aerosol (SIA), traffic emissions, photonucleation, coal combustion, residential heating and festival emissions. The DN-PMF enhanced the diel patterns of photonucleation and the two traffic factors by enlarging the differences between daytime peak values and nighttime concentrations. The municipal incinerator plant, traffic emissions, and coal combustion have cleaner and more clearly defined directionalities after dispersion normalization. Thus, dispersion normalized PMF is capable of enhancing the source emission patterns. After the COVID-19 lockdown began, PNC of traffic nucleation and traffic emissions decreased by 41% and 44%, respectively, while photonucleation produced more particles likely due to the reduction in the condensation sink. The significant changes in source emissions indicate a substantially reduced traffic volume after the implement of lockdown measures., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The multi-scale chemical characteristics and source apportionment of volatile organic compounds (VOCs) were analysed in Tianjin, China, using 1-hr resolution VOC-species data between November 1, 2018 and March 15, 2019. The average total VOC (TVOC) concentration was 30.6 ppbv during the heating season. The alkanes accounted for highest proportion of the TVOC, while the alkenes were the predominant species forming ozone, especially ethylene. Compared to the clean period, the concentration of acetylene during the haze events showed highest increase rate, followed by the ethane; and the concentrations and proportions of alkanes and alkenes were highest during the growth stage (GS) of haze events. The multi-scale apportionment results suggested petrochemical industry and solvent usage (PI/SU, 31.2%), vehicle emissions and liquefied petroleum gas (VE/LPG, 20.5%), and combustion emissions (CE, 19.1%) were the main VOC sources during the heating season. Compared to the clean period, the contributions of PI/SU, VE/LPG, CE, and refinery emissions notably increased during the haze events, while that of gasoline evaporation decreased. The contributions of PI/SU and RPI showed significantly increase during the GS of haze events, whereas most sources decreased during the dissipation stage of haze events. Diurnal-variations in source contributions during the haze events were clearer than the clean period, and the contributions of PI/SU, VE/LPG, and CE during the haze events were markedly higher at night. These findings provide valuable information to inform effective VOC control and prevention measures with specific relevance for the control of ozone pollution in Tianjin., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Factor analysis utilizes the covariance of compositional variables to separate sources of ambient pollutants like particulate matter (PM). However, meteorology causes concentration variations in addition to emission rate changes. Conventional positive matrix factorization (PMF) loses information from the data because of these dilution variations. By incorporating the ventilation coefficient, dispersion normalized PMF (DN-PMF) reduces the dilution effects. DN-PMF was applied to hourly speciated particulate composition data from a field campaign that included the start of the COVID-19 outbreak. DN-PMF sharpened the morning coal combustion and rush hour traffic peaks and lowered the daytime soil, aged sea salt, and waste incinerator contributions that better reflect the actual emissions. These results identified significant changes in source contributions after the COVID-19 outbreak in China. During this pandemic, secondary inorganic aerosol became the predominant PM 2.5 source representing 50.5% of the mean mass. Fireworks and residential burning (32.0%), primary coal combustion emissions (13.3%), primary traffic emissions (2.1%), soil and aged sea salt (1.2%), and incinerator (0.9%) represent the other contributors. Traffic decreased dramatically (70%) compared to other sources. Soil and aged sea salt also decreased by 68%, likely from decreased traffic.

Background: The recent approval of 4th generation HIV tests has forced many laboratories to decide whether to shift from 3rd to these tests. There are limited published studies on the comparative evaluation of these two different assays. We compare the performance of fourth-generation electrochemiluminescence immunoassay (ChIA) and third-generation enzyme linked immunosorbent assay (EIA) for human immunodeficiency virus (HIV) screening and gauge whether the shift from EIA to ChIA could be better in a multiethnic region of China., Methodology/principal Findings: We identified a large number of routine specimens (345,492) using two different assays from Jan 2008 to Aug 2011 in a teaching hospital with high sample throughput. Of the 344,596 specimens with interpretable HIV test results, 526(0.23%) of 228,761 using EIA and 303(0.26%) of 115,835 using ChIA were HIV-1 positive. The false-positive rate of EIA was lower than that of ChIA [0.03% vs. 0.08%, odds ratio 0.33 (95% confidence interval 0.24, 0.45)]. The positive predictive value (PPV) of EIA (89.6%) was significantly higher than that of ChIA (76.1%) (<0.001), reflecting the difference between the two assays. The clinical sensitivities of two assays in this study were 99.64% for EIA and 99.88% for ChIA., Conclusion: Caution is needed before shifting from 3rd to 4th generation HIV tests. Since none of these tests are perfect, different geographic and ethnic area probably require different considerations with regard to HIV testing methods, taking into account the local conditions.

During 2005, the filter samples of ambient PM10 from five sites and the source samples of particulate matter were collected in Kaifeng, Henan Province of China. Nineteen elements, water-soluble ions, total carbon (TC) and organic carbon (OC) contained in samples were analyzed. Seven contributive source types were identified and their contributions to ambient PM10 were estimated by chemical mass balance (CMB) receptor model. Weak associations between the concentrations of organic carbon and element carbon (EC) were observed during the sampling periods, indicating that there was secondary organic aerosol pollution in the urban atmosphere. An indirect method of "OC/EC minimum ratio" was applied to estimate the concentration of secondary organic carbon (SOC). The results showed that SOC contributed 26.2%, 32.4% and 18.0% of TC in spring, summer-fall and winter, respectively, and the annual average SOC concentration was 7.07 microg/m3, accounting for 5.73% of the total mass in ambient PM10. The carbon species concentrations in ambient PM10 were recalculated by subtracting SOC concentrations from measured concentrations of TC and OC to increase the compatibility of source and receptor measurements for CMB model.