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

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. [Display omitted] • A moving window evolving DN-PMF was applied to PM 2.5 composition dataset. • The applied method provides some uncertainty estimates for source contributions. • Wind dependent sources like dust and distant point sources have larger uncertainties than the other sources. Moving window evolving DN-PMF provides uncertainty estimates for source contributions. [ABSTRACT FROM AUTHOR]

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]

Source apportionments have become increasingly performed to determine the origins of ambient particulate pollution. The results can be helpful in designing mitigation strategies to improve air quality. Source specific particulate matter (PM) concentrations are also being used in health effects studies to be able to focus attention on those sources most likely to be responsible for the observed adverse health effects. In 2015, the World Health Organization (WHO) released its initial compilation of source apportionment studies published through August 2014. This initial database was described by Karagulian et al. (Atmospheric Environment120 (2015) 475–483). In the present report, a new compilation has been prepared of those apportionments published since 2014 through December 2019. In addition, the database has been expanded to include apportionments of heavy metals, water-soluble components, and carbonaceous components in ambient PM. As a result of this work, we have developed and presented some perspectives on source apportionment going forward. We also have made a series of recommendations for source apportionment studies and reporting them. It is essential for papers to provide a minimum set of information so that the study can be adequately assessed, and the results utilized by others in making policy decisions or as part of other scientific studies. Unlabelled Image • Comprehensive review of the PM 2.5 /PM 10 source apportionment literature since 2014 • Includes apportionments for heavy metals, water-soluble species, and carbonaceous PM • Major increase in source apportionment studies in China • Most commonly used source apportionment method is positive matrix factoriztion [ABSTRACT FROM AUTHOR]

Rotational ambiguity in factor analyses prevents users from obtaining accurate source apportionment results. The rotational space in positive matrix factorization (PMF) can be reduced by constraining the solution with a priori information such as source profiles. However, the only prior report on constraints using information on the source contributions was their use to ensure compatibility in the simultaneous analyses of PM 2.5 and PM 10 data. By combining data from three monitoring sites affected by a gear casting plant in Xi'an as an example, a methodology for improving the accuracy of PMF results by constraining source contributions using wind information was explored. Seven common factors derived from individual PMF analyses for each of the three sites (INDUS, URBAN, and RURAL) with different location characteristics, were then combined in a multisite PMF analysis. The factors were interpreted as nitrate with all site average contributions of 28.7%, sulfate (22.5%), coal combustion (19.3%), road traffic (12.8%), biomass burning (6.4%), soil (5.4%), and metallurgical industry (4.9%). Except for the INDUS site, contributions of metallurgical industry to the URBAN and RURAL sites were pulled down maximally to reduce the rotational space. The constrained solution substantially improved the results over the base run. The local and regional nature of the sources were identified by coefficient of divergence combined with Pearson correlation analysis, and further quantitatively estimated using Lenschow approach. On average, local sources contributed for 52.4% and 47.7% of the PM 2.5 mass concentrations at the INDUS and URBAN site respectively. The metallurgical industry showed the highest local contributions while sulfate was primarily regional. For the multisite analysis where there are considerable point source emissions, this methodology highlights the role of local wind directions to inform constraints on the results and obtaining more reliable solutions. Unlabelled Image • PM 2.5 composition measured at three sites were investigated using multisite PMF. • G-value constraints were developed with information of winds and point source location. • The constrained solution substantially improved the results over the base run. • The local and regional nature of the sources were estimated using Lenschow approach. [ABSTRACT FROM AUTHOR]

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 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. Image 1 • Temporal patterns for species and sources of PM 2.5 along with gaseous pollutants were discussed. • Sources contributions on clean days, and in haze events and different haze stages were analysed. • Secondary sources, coal combustion, industrial emissions were primary sources in haze events. • High impact of crustal dust was found on clean days and in the dissipation stages of haze events. Source apportionment of PM 2.5 using a positive matrix factorisation (PMF) model coupled with 1–hr resolution online air pollutant dataset, and applied in a most polluted city of China. [ABSTRACT FROM AUTHOR]

With the intensification of Chinese source control of air pollution, there is an urgent need for refined and rapid source apportionment techniques. A refined source apportionment method was constructed based on an off-line sampling dataset using a receptor model coupled with a source-oriented model, and the method was implemented in Shijiazhuang during the heating period. The refined results for source apportionment mainly included temporal, spatial, and source-category refinement data. The results indicated that the mean concentration of PM 2.5 during the heating period was 96 μg/m3. Organic carbon (OC) and NO 3 − were found to be the dominant species of PM 2.5 during the study. A high correlation was detected between elemental carbon (EC) and NO 3 − on polluted days, which was suggestive of the stagnant condition that accumulates EC and nitrate simultaneously. Secondary particle formation greatly promoted the occurrence of haze events. Secondary sources (34.9%), vehicle exhaust (18.6%), coal combustion (20.0%), industrial emissions (9.2%), crustal dust (9.7%), and biomass burning (7.6%) were the major sources during the heating period. The contributions of secondary sources and vehicle exhaust increased on polluted days, while those of coal combustion, industrial emissions and crustal dust decreased significantly. The contribution percentage of secondary sources from the southeast direction was basically the highest, while those of vehicle exhaust from the northwest or southeast directions were relatively higher as well, likely due to the distribution of traffic arteries. Based on the refined results for the source-category assessment, we found that the heating boilers (17.0%), non-road mobile (13.8%), diesel vehicles (10.4%), residential combustion (6.7%), road dust (5.5%), and architectural material industry (4.9%) were the major contributors to PM 2.5. There was some uncertainty in the distribution proportions of the refined results, which were derived based on the emission inventory and the results of CALPUFF model. • A refined source apportionment method for ambient PM 2.5 was constructed. • Contributions of secondary sources and vehicle exhaust increased on polluted days. • Heating boilers, non-road mobile, and diesel vehicles were major contributors. [ABSTRACT FROM AUTHOR]