PM2.5 Source Apportionment Using a Hybrid Environmental Receptor Model

The impacts of regional transport on anthropogenic source contributions of PM2.5 in a basin city, China

PM2.5 pollution events are often happened in urban agglomeration locates in mountain-basin regions due to the complex terra and intensive emissions. Source apportionment is essential for identifying the pollution sources and important for developing local mitigation strategies, however, it is influenced by regional transport. To understand how the regional transport influences the atmospheric environment of a basin, we connected the PM2.5 source contributions estimated by observation-based receptor source apportionment and the regional contributions estimated by a tagging technology in the comprehensive air quality model with extensions (CAMx) via an artificial neural network (ANNs). The result shows that the PM2.5 in Xi'an was from biomass burning, coal combustion, traffic related emissions, mineral dust, industrial emissions, secondary nitrate and sulfate. 48.8 % of the PM2.5 in study period was from Xi'an, then followed by the outside area of Guanzhong basin (28.2 %), Xianyang (14.6 %) and Weinan (5.8 %). Baoji and Tongchuan contributed trivial amount. The sensitivity analysis showed that the transported PM2.5 would lead to divergent results of source contributions at Xi'an. The transported PM2.5 from the outside has great a potential to alter the source contributions implying a large uncertainty of the source apportionment introduced when long-range transported pollutants arrived. It suggests that a full comprehension on the impacts of regional transport can lower the uncertainty of the local PM2.5 source apportionment and reginal collaborative actions can be of great use for pollution mitigation.

Contributions of non-tailpipe emissions to near-road PM2.5 and PM10: A chemical mass balance study

As the importance of non-tailpipe particles (NTP) over tailpipe emissions from urban traffic has been increasing, there is a need to evaluate NTP contributions to ambient particulate matter (PM) using representative source profiles. The Brake and Tire Wear Study conducted in Los Angeles, California in the winter of 2020 collected 64 PM2.5 and 64 PM10 samples from 32 pairs of downwind-upwind measurements at two near-road locations (I-5 in Anaheim and I-710 in Long Beach). These samples were characterized for inorganic and organic markers and, along with locally-developed brake wear, tire wear, and road dust source profiles, subject to source apportionment using the effective-variance chemical mass balance (EV-CMB) model. Model results highlighted the dominance of resuspended dust in both PM2.5 (23-33%) and PM10 (32-53%). Brake and tire wear contributed more to PM2.5 than tailpipe exhausts (diesel + gasoline) for I-5 (29-30% vs. 19-21%) while they were comparable for I-710 (15-17% vs. 15-19%). For PM10, the brake and tire wear contributions were 2-3 times the exhaust contributions. Different fleet compositions on and near I-5 and I-710 appeared to influence the relative importance of NTP and exhaust sources. The downwind-upwind differences in source contributions were often insignificant, consistent with small and/or nearly equal impacts of adjacent highway traffic emissions on the downwind and upwind sites. The utility of sole markers, such as barium and zinc, to predict brake and tire wear abundances in ambient PM is evaluated.

Dynamic harmonization of source-oriented and receptor models for source apportionment

Millions of premature mortalities are caused by the air pollution of fine particulate matter with aerodynamic diameters less than 2.5 μm (PM_2.5) globally per year. To effectively control the dominant emission sources and abate air pollution, source apportionment of PM_2.5 is normally conducted to quantify the contributions of various sources, but the results of different methods might be inconsistent. In this study, we dynamically harmonized the results from the two dominant source apportionment methods, the source-oriented and receptor models, by updating the emission inventories of primary PM_2.5 from the major sectors based on the Bayesian Inference. An adjoint model was developed to efficiently construct the source-receptor sensitivity matrix, which was the critical information for the updates, and depicted the response of measurements to the changes in the emissions of various sources in different regions. The harmonized method was applied to a measurement campaign in Beijing from January to February 2021. The results suggested a significant reduction of primary PM_2.5 emissions in Beijing. Compared with the baseline emission inventory of 2017, the primary PM_2.5 emissions from the local residential combustion and industry in Beijing had significantly declined by about 90 % during the investigated period of the year, and the traffic emission decreased by about 50 %. The proposed methods successfully identified the temporally dynamic changes in the emissions induced by the Spring Festival. The methods could be a promising pathway for the harmonization of source-oriented and receptor source apportionment models.

Apportionment of Vehicle Fleet Emissions by Linear Regression, Positive Matrix Factorization, and Emission Modeling

Real-world emission factors for different vehicle types and their contributions to roadside air pollution are needed for air-quality management. Tunnel measurements have been used to estimate emission factors for several vehicle types using linear regression or receptor-based source apportionment. However, the accuracy and uncertainties of these methods have not been sufficiently discussed. This study applies four methods to derive emission factors for different vehicle types from tunnel measurements in Hong Kong, China: (1) simple linear regressions (SLR); (2) multiple linear regressions (MLR); (3) positive matrix factorization (PMF); and (4) EMission FACtors for Hong Kong (EMFAC-HK). Separable vehicle types include those fueled by liquefied petroleum gas (LPG), gasoline, and diesel. PMF was the most useful, as it simultaneously seeks source profiles and source contributions. Diesel-, gasoline-, and LPG-fueled vehicle emissions accounted for 52%, 10%, and 5% of PM2.5 mass, respectively, while ammonium sulfate (~20%), ammonium nitrate (6%), and road dust (7%) were also large contributors. MLR exhibited the highest relative uncertainties, typically over twice those determined by SLR. EMFAC-HK has the lowest relative uncertainties due to its assumption of a single average emission factor for each pollutant and each vehicle category under specific conditions. The relative uncertainties of SLR and PMF are comparable.

The Heavy Particulate Matter Pollution During the COVID-19 Lockdown Period in the Guanzhong Basin, China

Nationwide restrictions on human activities (lockdown) in China since 23 January 2020, to control the 2019 novel coronavirus disease pandemic (COVID-19), has provided an opportunity to evaluate the effect of emission mitigation on particulate matter (PM) pollution. The WRF-Chem simulations of persistent heavy PM pollution episodes from 20 January to 14 February 2020, in the Guanzhong Basin (GZB), northwest China, reveal that large-scale emission reduction of primary pollutants has not substantially improved the air quality during the COVID-19 lockdown period. Simultaneous reduction of primary precursors during the lockdown period only decreases the near-surface PM_2.5 mass concentration by 11.6% (12.6 μg m^-3), but increases ozone (O₃) concentration by 9.2% (5.5 μg m^-3) in the GZB. The primary organic aerosol and nitrate are the major contributor to the decreased PM_2.5 in the GZB, with the reduction of 28.0% and 21.8%, respectively, followed by EC (10.1%) and ammonium (7.2%). The increased atmospheric oxidizing capacity by the O₃ enhancement facilitates the secondary aerosol (SA) formation in the GZB, increasing secondary organic aerosol and sulphate by 6.5% and 3.3%, respectively. Furthermore, sensitivity experiments suggest that combined emission reduction of NO_X and VOCs following the ratio of 1:1 is conducive to lowering the wintertime SA and O₃ concentration and further alleviating the PM pollution in the GZB.

Brownness of Organic Aerosol over the United States: Evidence for Seasonal Biomass Burning and Photobleaching Effects

Light-absorptivity of organic aerosol may play an important role in visibility and climate forcing, but it has not been assessed as extensively as black carbon (BC) aerosol. Based on multiwavelength thermal/optical analysis and spectral mass balance, this study quantifies BC for the U.S. Interagency Monitoring of Protected Visual Environments (IMPROVE) network while developing a brownness index (γ_Br) for non-BC organic carbon (OC*) to illustrate the spatiotemporal trends of light-absorbing brown carbon (BrC) content. OC* light absorption efficiencies range from 0 to 3.1 m² gC^-1 at 405 nm, corresponding to the lowest and highest BrC content of 0 and 100%, respectively. BC, OC*, and γ_Br explain >97% of the variability of measured spectral light absorption (405-980 nm) across 158 IMPROVE sites. Network-average OC* light absorptions at 405 nm are 50 and 28% those for BC over rural and urban areas, respectively. Larger organic fractions of light absorption occur in winter, partially due to higher organic brownness. Winter γ_Br exhibits a dramatic regional/urban-rural contrast consistent with anthropogenic BrC emissions from residential wood combustion. The spatial differences diminish to uniformly low γ_Br in summer, suggesting effective BrC photobleaching over the midlatitudes. An empirical relationship between BC, ambient temperature, and γ_Br is established, which can facilitate the incorporation of organic aerosol absorptivity into climate and visibility models that currently assume either zero or static organic light absorption efficiencies.

Impacts of primary emissions and secondary aerosol formation on air pollution in an urban area of China during the COVID-19 lockdown

Restrictions on human activities were implemented in China to cope with the outbreak of the Coronavirus Disease 2019 (COVID-19), providing an opportunity to investigate the impacts of anthropogenic emissions on air quality. Intensive real-time measurements were made to compare primary emissions and secondary aerosol formation in Xi'an, China before and during the COVID-19 lockdown. Decreases in mass concentrations of particulate matter (PM) and its components were observed during the lockdown with reductions of 32-51%. The dominant contributor of PM was organic aerosol (OA), and results of a hybrid environmental receptor model indicated OA was composed of four primary OA (POA) factors (hydrocarbon-like OA (HOA), cooking OA (COA), biomass burning OA (BBOA), and coal combustion OA (CCOA)) and two oxygenated OA (OOA) factors (less-oxidized OOA (LO-OOA) and more-oxidized OOA (MO-OOA)). The mass concentrations of OA factors decreased from before to during the lockdown over a range of 17% to 58%, and they were affected by control measures and secondary processes. Correlations of secondary aerosols/ΔCO with Ox (NO2 + O3) and aerosol liquid water content indicated that photochemical oxidation had a greater effect on the formation of nitrate and two OOAs than sulfate; however, aqueous-phase reaction presented a more complex effect on secondary aerosols formation at different relative humidity condition. The formation efficiencies of secondary aerosols were enhanced during the lockdown as the increase of atmospheric oxidation capacity. Analyses of pollution episodes highlighted the importance of OA, especially the LO-OOA, for air pollution during the lockdown.

Comparison of five methodologies to apportion organic aerosol sources during a PM pollution event

This study presents a comparison of five methodologies to apportion primary (POA) and secondary organic aerosol (SOA) sources from measurements performed in the Paris region (France) during a highly processed PM pollution event. POA fractions, estimated from EC-tracer method and positive matrix factorization (PMF) analyses, conducted on measurements from PM₁₀ filters, aerosol chemical speciation monitor (ACSM) and offline aerosol mass spectrometry (AMS), were all comparable (2.2-3.7 μg m^-3 as primary organic carbon (POC)). Associated relative uncertainties (measurement + model) on POC estimations ranged from 8 to 50%. The best apportionment of primary traffic OA was achieved using key markers (EC and 1-nitropyrene) in the chemical speciation-based PMF showing more pronounced rush-hour peaks and greater correlation with NO_x than other traffic related POC factors. All biomass burning-related factors were in good agreement, with a typical diel profile and a night-time increase linked to residential heating. If PMF applied to ACSM data showed good agreement with other PMF outputs corrected from dust-related factors (coarse PM), discrepancies were observed between individual POA factors (traffic, biomass burning) and directly comparable SOA factors and highly oxidized OA. Similar secondary organic carbon (SOC) concentrations (3.3 ± 0.1 μg m^-3) were obtained from all approaches, except the SOA-tracer method (1.8 μg m^-3). Associated uncertainties ranged from 14 to 52% with larger uncertainties obtained for PMF-chemical data, EC- and SOA-tracer methods. This latter significantly underestimated total SOA loadings, even including biomass burning SOA, due to missing SOA classes and precursors. None of the approaches was able to identify the formation mechanisms and/or precursors responsible for the highly oxidized SOA fraction associated with nitrate- and/or sulfate-rich aerosols (35% of OA). We recommend the use of a combination of different methodologies to apportion the POC/SOC concentrations/contributions to get the highest level of confidence in the estimates obtained.

A comprehensive study on ozone pollution in a megacity in North China Plain during summertime: Observations, source attributions and ozone sensitivity

Tropospheric ozone (O₃) pollution has been becoming prominent in North China Plain (NCP) in China since last decade. In order to clarify the source contribution and formation mechanism of O₃, the critical precursors of volatile organic compounds (VOCs) were measured with both on-line and off-line methods in Luoyang City in summer of 2019. The concentrations of nitrogen oxides (NO_x, sum of NO and NO₂) and O₃ were simultaneously monitored. Fifty-seven VOCs measured in U.S. Photochemical Assessment Monitoring Station (PAMS) showed daily concentrations in a range of 14.5 ± 5.33 to 29.2 ± 11.2 ppbv in Luoyang, which were comparable with those in other Chinese megacities. The mass compositions of VOCs were determined, with comparatively low proportions of alkanes (<50%) but high fractions of photoreactive alkenes and alkyne. Source apportionment of VOCs was conducted by Hybrid Environmental Receptor Model (HERM). The results indicated that industrial (38.5%) and traffic (32.0%) were the two dominated pollution sources of VOCs in the urban, while the biogenic and residential sources had contributions of 15.8% and 13.8%, respectively. To further measure the O₃ formation sensitivity and its source attribution, the WRF-CHEM model was adopted in this study. The variation of O₃ between the observation and the stimulation using the local emission inventory showed an index of agreement (IOA) of 0.85. The simulation conducted by WRF-CHEM indicated an average of 43.5% of the O₃ was associated with the regional transportation, revealing the importance of inter-regional prevention and control policy. Traffic and biogenic emissions were the two major pollution sources to an O₃ episode occurred from July 21 to July 27, 2019 (when O₃ concentration over 150 μg m^-3) in Luoyang, with average contributions of 22.9% and 18.3%, respectively. The O₃ isopleths proved that its formation in the atmosphere of Luoyang was in transitional regime and collectively controlled by both VOCs and NO_x. This was different from the observations in main cities of NCP before implantations of strict emission controls. The isopleths additionally designated that the O₃ formation regime would move forward or shift to NO_x regime after a reduction of over 45% during the episode. Similar patterns were also reported in other Chinese megacities such as Beijing and Shanghai, due to the tightening of the NO_x control policies. Our results do support that the simultaneous controls of NO_x and VOCs were effective in reductions of tropospheric O₃ in Luoyang. Meanwhile, joint regional control policies on the emissions of NO_x and VOCs can potentially overwhelm the current O₃ pollutions in China.

Improving apportionment of PM2.5 using multisite PMF by constraining G-values with a priori information

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 prioriinformation 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 PM₁₀ 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.

Non-Negligible Stack Emissions of Noncriteria Air Pollutants from Coal-Fired Power Plants in China: Condensable Particulate Matter and Sulfur Trioxide

In this study, we investigated the emission characteristics of condensable particulate matter (CPM) and sulfur trioxide (SO₃) simultaneously through ammonia-based/limestone-based wet flue gas desulfurization (WFGD) from four typical coal-fired power plants (CFPPs) by conducting field measurements. Stack emissions of filterable particulate matter (FPM) all meet the Chinese ultralow emission (ULE) standards, whereas CPM concentrations are prominent (even exceed 10 mg/Nm³ from two CFPPs). We find that NH₄⁺ and Cl^- increase markedly through the ammonia-based WFGD, and SO₄^2- is generally the main ionic component, both in CPM and FPM. Notably, the occurrence of elemental Se in FPM and CPM is significantly affected by WFGD. Furthermore, the established chemical profiles in FPM and CPM show a distinct discrepancy. In CPM, the elemental S mainly exists as a sulfate, and the metallic elements of Na, K, Mg, and Ca mainly exist as ionic species. Our results may indicate that not all SO₃ are included in CPM and they co-exist in stack plume. With the substantial reduction of sulfur dioxide (SO₂), S distributed in SO₃, CPM, and FPM becomes non-negligible. Finally, the emission factors of CPM and SO₃ under typical ULE technical routes fall in the ranges of 74.33-167.83 and 48.76-86.30 g/(t of coal) accordingly.

The haze extreme co-movements in Beijing-Tianjin-Hebei region and its extreme dependence pattern recognitions

Extreme haze was often observed at many locations in Beijing-Tianjin-Hebei region within several hours when they occurred, which is referred to as extreme co-movements and extreme dependence in statistics. This article applies tail quotient correlation coefficient to explore the temporal and spatial extreme dependence patterns of haze in this region. Hourly PM2.5 station-level data during 2014-2018 are used, and the results show that the tail quotient correlation coefficient between stations increases with month. Specifically, the simultaneous extreme dependence was strong in the fourth season, while the haze was severe. In the first season, while the haze was also severe, the extreme hazes only show strong co-movements with a time difference. These observations lead to the study of two special scenarios, that is, the concurrence/extreme dependence of the worst extreme haze and its lag effects. City clusters suffering simultaneous extreme haze or with certain time difference as well as the most frequently co-movement cities are identified. The extreme co-movements of these cities and the reasons for their occurrences have strong implications for improving the PM2.5 joint prevention and control in the Beijing-Tianjin-Hebei region. The importance of lag effects is also reflected in the precedence order of the extreme haze's appearance. It is especially useful when setting the mechanism of the early warning system which can be triggered by the first appearance of extreme haze. The precedence orders also avail in investigating the transmission path of the haze, based on which more precise meteorological models can be made to benefit the haze forecasting of the region.