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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 The two industrial source types showed sharp drops after the 2013 mandated controls went into effect to phaseout outdated iron/steel production and enforce tighter emission standards for these industries.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.)

Photochemical losses of volatile organic compounds (VOCs) and uncertainties in calculated factor profiles can reduce the accuracy of source apportionment by positive matrix factorization (PMF). We developed an improved PMF method (termed ICLP-PMF) that estimated the reaction-corrected ("initial") concentrations of VOCs. Source profiles from literature provided constraints. ICLP-PMF evaluated the vehicular emission contributions to hourly speciated VOC data from December 2020 to March 2021 and estimated gasoline and diesel vehicles contributions to Tianjin's VOC concentrations around the Chinese Spring Festival (SF). The average observed and initial total VOCs (TVOCs) concentrations were 24.2 and 42.9 ppbv, respectively. Alkanes were the highest concentration VOCs while aromatics showed the largest photochemical losses during the study period. Literature gasoline and diesel profiles from representative Chinese cities were constructed and provided constraints. Source apportionment was performed using ICLP-PMF method and three other PMF approaches. Photochemical losses of alkenes and aromatic hydrocarbons induced differences between calculated factor profiles and literature profiles. Using observed concentrations and unconstrained profiles produced underestimated SF contributions (∼121% and 72% for gasoline and diesel vehicles, respectively). According to the ICLP-PMF results, the contributions of gasoline and diesel vehicles during the SF were 25.6% and 23.2%, respectively, lower than those before and after the SF. No diel diesel vehicle contribution variations were found during the SF likely due to the decline in truck activity north of the study site during the holiday period., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Conventional factor analyses can present problems in cases with changing numbers of sources and/or time-dependent source compositions. There is also lack of a reliable method to estimate uncertainties in the source contributions derived by positive matrix factorization (PMF). Applying a moving window evolving PMF to hourly PM 2.5 composition dataset from a field campaign in Tianjin China that included the Spring and Lantern Festivals and the start of COVID-19 pandemic has substantially improved the apportionment compared to a conventional analysis using the entire data. Festival-related sources (e.g., fireworks and residential burning here) have been clearly identified and estimated during both the Spring and Lantern Festivals. During this period, the sources changed because the time period overlaps with the outbreak of COVID-19 and related reductions in activity during the lockdown that began on Lunar New Year. Multiple PMF runs providing source contribution estimates made it possible to estimate the uncertainties in these values. Our results show that winds-dependent sources like dust and distant point sources have larger uncertainties than the other sources. Compared with conventional PMF analyses, the current method may better reflect the actual emissions as well as being able to estimate uncertainties. Thus, this approach appears to be an improvement if the appropriate data are available., (Copyright © 2021 Elsevier Ltd. 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.)

Samples of ambient PM 2.5 were collected in the Qingdao harbor area between 21 March and May 25, 2016, and analyzed to investigate the compositions and sources of PM 2.5 and to assess source-specific selected toxic element health risks to workers via a combination of positive matrix factorization (PMF) and health risk (HR) assessment models. The mean concentration of PM 2.5 in harbor area was 48 μg m -3 with organic matter (OM) dominating its mass. Zn and V concentrations were significantly higher than the other selected toxic elements. The hazard index (HI) and cancer risk (R i ) of all selected toxic elements were lower than the United States Environmental Protection Agency (USEPA) limits. There were no non-cancer and cancer risks for workers in harbor area. The contributions from industrial emissions (IE), ship emissions (SE), vehicle emissions (VE), and crustal dust and coal combustion (CDCC) to selected toxic elements were 39.0%, 12.8%, 24.0%, and 23.0%, respectively. The HI values of selected toxic elements from IE, CDCC, SE, and VE were 1.85 × 10 -1 , 7.08 × 10 -2 , 6.36 × 10 -2 , and 3.37 × 10 -2 , respectively; these are lower than the USEPA limits. The total cancer risk (R t ) value from selected toxic elements in CDCC was 2.04 × 10 -7 , followed by IE (6.40 × 10 -8 ), SE (2.26 × 10 -8 ), and VE (2.18 × 10 -8 ). CDCC and IE were the likely sources of cancer risk in harbor area. The Bo Sea and coast were identified as the likely source areas for health risks from IE via potential source contribution function (PSCF) analysis based on the results of PMF-HR modelling. Although the source-specific health risks were below the recommended limit values, this work illustrates how toxic species in PM 2.5 health risks can be associated with sources such that control measures could be undertaken if the risks warranted it., 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.)

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.)

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/m 3 during the heating period, and NO 3 - and organic carbon (OC) were the dominant species. The concentrations and percentages of NO 3 - , SO 4 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., 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.)

In Xi'an, a city that frequently experiences serious PM pollution in northern China, 1476 PM 10 and 1464 PM 2.5 valid daily filter samples were collected at six sites from December 2014 to November 2015 and analyzed for 29 species. The annual mean PM 10 and PM 2.5 concentrations were 149.4 ± 93.1, 108.0 ± 70.9 μg/m 3 , respectively. Organic carbon (OC) is the predominant PM 2.5 component while crustal material predominated in PM 10 . Sulfate concentrations, which was the largest component in Xi'an PM in previous studies, were lower than nitrate. Winter sulfate, OC, and elemental carbon (EC) have decreased since 2003, while nitrate remained constant in recent years and the ratio of NO 3 - /SO 4 2- increased from 0.4 in 2006 to 1.3 in 2014. This result suggests that the motor vehicle contribution to PM has increased relative to coal-fired power plant emissions over the past decade. The mass fractions of crustal material, sulfate, and EC in PM 2.5 decreased as the PM 2.5 concentrations increased from "clean" days (<50 μg/m 3 ) to the highest values, while nitrate significantly increased. Despite forming through secondary reactions, the high concentrations of SOC and SO 4 2- in winter are attributed to primary emissions and particularly to residential heating and cooking with coal. Primary SOC and SO 4 2- accounted for 33% and 42% of their total PM 2.5 concentrations in winter, respectively. Therefore, control measures applied to these primary sources can substantially improve air quality., (Copyright © 2018. Published by Elsevier Ltd.)

In recent years, China has experienced severe and persistent air pollution associated with rapid urbanization and climate change. Three years' time series (January 2014 to December 2016) concentrations data of air pollutants including particulate matter (PM 2.5 and PM 10 ) and gaseous pollutants (SO 2 , NO 2 , CO, and O 3 ) from over 1300 national air quality monitoring sites were studied to understand the severity of China's air pollution. In 2014 (2015, 2016), annual population-weighted-average (PWA) values in China were 65.8 (55.0, 50.7) μg m -3 for PM 2.5 , 107.8 (91.1, 85.7) μg m -3 for PM 10 , 54.8 (56.2, 57.2) μg m -3 for O 3 &#95;8 h, 39.6 (33.3, 33.4) μg m -3 for NO 2 , 34.1 (26, 21.9) μg m -3 for SO 2 , 1.2 (1.1, 1.1) mg m -3 for CO, and 0.60 (0.59, 0.58) for PM 2.5 /PM 10 , respectively. In 2014 (2015, 2016), 7% (14%, 19%), 17% (27%, 34%), 51% (67%, 70%) and 88% (97%, 98%) of the population in China lived in areas that meet the level of annual PM 2.5 , PM 10 , NO 2 , and SO 2 standard metrics from Chinese Ambient Air Quality Standards-Grade II. The annual PWA concentrations of PM 2.5 , PM 10 , O 3 &#95;8 h, NO 2 , SO 2 , CO in the Northern China are about 40.4%, 58.9%, 5.9%, 24.6%, 96.7%, and 38.1% higher than those in Southern China, respectively. Though the air quality has been improving recent years, PM 2.5 pollution in wintertime is worsening, especially in the Northern China. The complex air pollution caused by PM and O 3 (the third frequent major pollutant) is an emerging problem that threatens the public health, especially in Chinese mega-city clusters. NOx controls were more beneficial than SO 2 controls for improvement of annual PM air quality in the northern China, central, and southwest regions. Future epidemiologic studies are urgently required to estimate the health impacts associated with multi-pollutants exposure, and revise more scientific air quality index standards., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

From November 2014 to October 2015, the concentrations of volatile organic compounds (VOCs), O 3 and NOx were simultaneously monitored by using online instruments at the air monitoring station belonged to Tianjin Environmental Protection Bureau (TEPB). The results indicated that VOCs concentrations were higher in autumn and lower in spring, while O 3 concentrations were higher in summer, and lower in winter. The diurnal variations of VOCs and NOx (NO 2 plus NO) showed opposite tendency comparing to that of O 3 . The concentrations of alkanes were higher (the average of 18.2 ppbv) than that of aromatics (5.3 ppbv) and alkenes (5.2 ppbv), however, the alkenes and aromatics made larger contributions to ozone because of their high reactivity. Tianjin belonged to the VOC-limited region during most of seasons (except summer) according to the VOC/NOx ratios (the 8:1 threshold). The automobile exhaust, industrial emission, liquefied petroleum gas/natural gas (LPG/NG), combustion, gasoline evaporation, internal combustion engine emission and solvent usage were identified as major sources of VOCs by Positive Matrix Factorization (PMF) model in Tianjin, and the contributions to VOCs for the entire year were 23.1%, 19.9%, 18.6%, 10.6%, 8.7%, 5.4% and 4.7%, respectively. The conditional probability function (CPF) analysis indicated that the contributing directions of automobile exhaust and industrial emission were mainly affected by source distributions, and that of other sources might be mainly affected by wind direction. The backward trajectory analysis indicated that the trajectory of air mass originated from Mongolia, which reflected the features of large-scale and long-distance air transport, and that of beginning in Jiangsu, Shandong and Tianjin, which showed the features of small-scale and short-distance. Tianjin, Beijing, Hebei and Northwest of Shandong were identified as major potential source-areas of VOCs by using potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) models., (Copyright © 2016 Elsevier Ltd. All rights reserved.)