Drivers of the rapid rise and daily-based accumulation in PM1

STOTEN-143394; No of Pages 9 Science of the Total Environment xxx (xxxx) xxx

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Drivers of the rapid rise and daily-based accumulation in PM1 Junting Zhong a,b, Xiaoye Zhang a,c,⁎, Yangmei Zhang a,⁎, Yaqiang Wang a, Zhouxiang Zhang d, Xiaojing Shen a, Junying Sun a, Lei Zhang a, Ke Gui a, Sanxue Ren a, Huarong Zhao a, Yubin Li e, Zhiqiu Gao e,f a

State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China University of Chinese Academy of Sciences, Beijing 100049, China Center for Excellence in Regional Atmospheric Environment, IUE, Chinese Academy of Sciences, Xiamen 361021, China d Hubei Ecological Environment Monitoring Center Station, Wuhan 430070, China e Nanjing University of Information Science & Technology, Nanjing 210044, China f Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• PM1 rapid rising is mainly driven by increased inversions and decreased vertical fluxes during the day-night transition. • A part of rise in organics is ascribed to an increase of coal combustion at midnight. • The daily-based accumulation of PM1 is attributed to day-to-day vertical meteorological variability. • BL meteorological variability can explain 71% variances of PM1 resolved by a multiple linear regression model.

a r t i c l e

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Article history: Received 25 August 2020 Received in revised form 21 October 2020 Accepted 27 October 2020 Available online xxxx Editor: Pingqing Fu Keywords: PM1 composition Rapid rise Daily-based accumulation Meteorological variability Coal combustion

a b s t r a c t Submicron particle matter (PM1) that rapidly reaches exceedingly high levels in several or more hours in the North China Plain (NCP) has been threating~400 million individuals' health for decades. The precise cause of the rapid rise in PM1 remains uncertain. Based on sophisticated measurements in PM1 characterizations and corresponding boundary-layer (BL) meteorology in the NCP, it demonstrates that this rising is mainly driven by BL meteorological variability. Large increases in near-ground inversions and decreases in vertical heat/momentum fluxes during the day-night transition result in a significant reduction in mixing space. The PM1 that is vertically distributed before accumulates at the near-ground and then experiences a rapid rise. Besides meteorological variability, a part of the rise in organics is ascribed to an increase of coal combustion at midnight. The daily-based accumulation of PM1 is attributed to day-to-day vertical meteorological variability, particularly diminishing mixing layer height exacerbated by aerosol-radiation feedback. Resolved by a multiple linear regression model, BL meteorological variability can explain 71% variances of PM1. In contrast, secondary chemical reactions facilitate the daily-based accumulation of PM1 rather than the rapid rise. Our results show that BL meteorological variability plays a dominant role in PM1 rising and day-to-day accumulation, which is crucial for understanding the mechanism of heavy pollution formation. © 2020 Elsevier B.V. All rights reserved.

⁎ Corresponding authors at: State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, China. E-mail addresses: [email protected] (X. Zhang), [email protected] (Y. Zhang).

1. Introduction Due to tremendous anthropogenic emissions and frequent unfavorable meteorological conditions in winter (Wang et al., 2018), the North

https://doi.org/10.1016/j.scitotenv.2020.143394 0048-9697/© 2020 Elsevier B.V. All rights reserved.

Please cite this article as: J. Zhong, X. Zhang, Y. Zhang, et al., Drivers of the rapid rise and daily-based accumulation in PM1, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2020.143394

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the relative importance of meteorology and chemistry, we conducted online measurements of aerosol composition and comprehensive observations of horizontal and vertical meteorological conditions during a severely polluted period in a rural site in the NCP. As one of representative sites of the China Atmosphere Watch Network (CAWNET), this site gains a significant advantage in monitoring the chemical composition (Zhang et al., 2015). In this study, we present an overview of chemical composition and determine the relative contributions of boundarylayer (BL) meteorology and secondary reactions to the rapid rise of PM1 and daily-based accumulations. These results are crucial for understanding the mechanism of heavy pollution formation.

China Plain has experienced severe aerosol pollution dominated by particulate matter with diameters less than 2.5 μm (PM2.5). These particles are detrimental to human health and conducive to reducing atmospheric visibility and thus have received considerable attention. 70– 90% of PM2.5 in polluted regions is composed of submicron PM (PM1) (Zhang et al., 2009; Zhang et al., 2018), which can affect human health and worsen visibility more severely due to its smaller size (Chow et al., 2002). As a result, PM1, a major part of PM2.5, is increasingly becoming a focus of research. To reduce the adverse effects of fine or submicron particles, researchers have carried out extensive studies to identify the causes of aerosol pollution and reduce PM effectively (R. Zhang et al., 2013; X. Zhang et al., 2013; Guo et al., 2014; Gui et al., 2019). Nevertheless, air pollution mitigation is still a considerable challenge due to tremendous emission sources and complicated formation mechanisms. To achieve a better understanding of aerosol sources and evolution processes of pollution and then develop effective emission control strategies, many recent studies have been carried out from the perspective of chemical characteristics and source analysis. Recently, the online measurements of aerosol chemical composition have been conducted widely in Beijing using the Aerodyne Aerosol Mass Spectrometer (AMS)/Aerosol Chemical Speciation Monitor (ACSM), and the former one is a unique instrument to observe the chemical composition and size distributions simultaneously. With high sensitivity and reliable performance, AMS/ACSM have become key tools to investigate chemical composition gradually and showed − − + that sulfate (SO2− 4 ), nitrate (NO3 ), ammonium (NH3 ), chloride (Cl ), organic aerosols (OA), and black carbon (BC) are major components of PM1 (Sun et al., 2013a; Sun et al., 2013b; Sun et al., 2014; Jiang et al., 2015; Sun et al., 2015; Wang et al., 2015; Yang et al., 2015; Sun et al., 2016a; Sun et al., 2016b; Pan et al., 2017; Sun et al., 2018; Wang et al., 2018; Zhao et al., 2019; Y. Zhang et al., 2013; Z. Zhang et al., 2017; Zhang et al., 2018; Hu et al., 2016; Hu et al., 2017; Schmitt, 2018). For example, SO2− was a major inorganic component, which 4 accounted for 12– 22% of non-refractory submicron particulate matter (NR-PM1) before 2017 (Hu et al., 2016; Schmitt, 2018; Sun et al., 2013b; J.K. Zhang et al., 2014; Sun et al., 2015; Xu et al., 2019; Zhou et al., 2018; Zhang et al., 2018), but it has been substantially reduced to less than 8% since 2017 (Li et al., 2019; Zhou et al., 2019). In contrast, NO− 3 has become the most significant inorganic component with its proportion over 30% in NR-PM1 since 2017 (Zhou et al., 2019; Li et al., 2019). Around half of the NR-PM1 was composed of OA. Secondary OA (SOA) was the most significant component in most cases and contributed 31– 64% of OA, whereas primary OA (POA) contributed to 36– 69% of OA (Sun et al., 2013b; Sun et al., 2014; J.K. Zhang et al., 2014; Sun et al., 2015; Sun et al., 2018; Hu et al., 2017; Zhang et al., 2018). The largest component of POA changed from coal combustion OA (CCOA) before 2017 to biomass burning OA (BBOA) in 2019 (Zhao et al., 2019; Li et al., 2019; Sun et al., 2013a; Sun et al., 2013b; Sun et al., 2018). Based on these research, previous studies also have demonstrated that anthropogenic emission sources, unfavorable meteorological conditions, and secondary reactions are major contributors to aerosol pollution (R. Zhang et al., 2013; X. Zhang et al., 2013; Wang et al., 2014; Guo et al., 2014; Zheng et al., 2018; Zhang et al., 2019). Anthropogenic emission sources are the dominant drivers of PM's longterm variations (Zheng et al., 2018; Zhang et al., 2019). In the short term, with little change in emissions, PM variations were impacted by unfavorable meteorological conditions and secondary chemical reactions dramatically (Guo et al., 2014; Wang et al., 2014; X. Zhang et al., 2013; Zhong et al., 2017; Zhong et al., 2018a). However, these two contributors' relative importance remains controversial for the rapid rise in PM1 that reaches exceedingly high levels in several hours. Some studies attributed this rising to nucleation and growth by multiphase chemical processes (Guo et al., 2014; Wang et al., 2014). Nonetheless, other studies considered the main cause of rising as unfavorable meteorological conditions, i.e., regional transport and boundary layer variations (Zheng et al., 2015; Hua et al., 2016; Zhong et al., 2017). To investigate

2. Experimental method 2.1. Sampling site and instruments The Gucheng Station (39.16°N, 115.74°E; 15.2 m a.s.l.), a rural background site in Dingxing County, Hebei Province, is located 40 km northeast away from Baoding City and 120 km southwest from Beijing (Fig. 1a). Having a certain distance from large cities, this station is surrounded by farmland and sporadic villages and away from strong industrial emission sources (Shen et al., 2018). A set of commercial instruments were employed to measure the aerosol chemical characteristics, near ground fluxes, vertical variables, and conventional meteorological factors in three labs within a horizontal distance of 50 m from November 2016 to January 2017. The aerosol instruments were placed on the second floor of a two-story building, next to an ecological–meteorological observation system to record meteorological factors. The eddy correlation flux measurement system was set up at a 4 meter-height open platform. A series of vertical meteorological observation systems were installed on a five-story tower to monitor the winds, temperature, and relative humidity (RH) of 30, 20, 10, 2, and 1 m, respectively. During the campaign, atmospheric air was filtered and collected through a cyclone (16.7 L min−1) of particulate matter with diameters less than 10 μm and dried to less than 30% RH with an automatic aerosol dryer unit (Tuch et al., 2009) located on the roof of the two-story building. Through a sample inlet and splitter, PM10 was introduced to different aerosol instruments in the laboratory with the interior temperature at 20 °C and RH between 40% and 60%. A High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HRToF-AMS) from Aerodyne Research Inc., USA, was deployed to measure mass concentrations and size distributions of different components in − − + NR-PM1, including SO2− 4 , NO3 , NH4 , and Cl , and OA. The detailed principles and description of this instrument can be found in DeCarlo et al. (2006) and (Kimmel et al., 2011). The HR-ToF-AMS operated periodically at 2 minute intervals, including 1 min in the V-mode, composed of 3 cycles of 10 s mass spectra (MS) and 10 s particle ToF (PToF), and 1 min in the W-mode only with MS. A Multi-Angle Absorption photometer (MAAP, Model 5012, Thermo Fisher Scientific Inc., USA) was deployed to obtain BC concentration through measuring the absorption coefficient (σabs, 637) at a length of 637 nm with 5-min time resolution Müller et al. (2011). Additionally, a Twin Differential Mobility Particle Sizer (TDMPS) developed by the Leibniz Institute for Tropospheric Research (TROPOS), Germany, was deployed to measure particle number size distributions (PNSD) in the range of 4– 850 μm at 10 minute intervals with two condensing particle counters (CPC) and two differential mobility analyzers (DMA). Detailed calibration and description could be found in Wiedensohler et al. (2012) and Shen et al. (2018). The eddy correlation flux measurement system, mainly composed of a Three-Dimensional Ultrasonic Anemometer CSAT-3 (Campbell Scientific Incorporation) and an Open Path CO2/H2O Infrared Gas Analyzer LI7500 (LI-COR Incorporation), was deployed to measure near ground sensible/latent heat fluxes and momentum fluxes at 30 minute intervals. A detailed description of this system could be found in Liu et al. (2019). An automatic weather station observed meteorological factors 2

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HPE1

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Red-alert HPE

HPE2

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Fig. 1. (a) Location of the sampling site – Gucheng (© OpenStreetMap contributors 2020. Distributed under a Creative Commons BY-SA License); (b) time series of PM1 composition of three-month field measurements from November 2016 to January 2017; and (c) average chemical composition of PM1 and organics for the entire measurements.

signals of some representative tracers (i.e. C3H7, m/z 43; C4H7 m/z 55; C4H9, m/z 57; C2H4O2, m/z 60), as well as comparison with simultaneous gaseous components, five OA factors, which were composed of three POA factors, including Hydrocarbon Organic Aerosol (HOA), BBOA, CCOA, and two SOA factors, including low volatility oxygenated OA (LV-OOA) and semi-volatile oxygenated OA (SV-OOA), were revolved.

at ground, including wind speed, wind direction, temperature, RH, pressure, shortwave radiation, longwave radiation, and total radiation at 1 minute intervals. The meteorological factors at 1, 2, 10, 20, and 30 m were monitored by automatic weather stations at 1 minute intervals. Temperature differences between different layers were used to represent the effects of near-ground inversions. Mixing layer height (MLH) was calculated based on the total cloud cover, low cloud cover, and wind speed according to the Technical Guidelines for Environmental Impact Assessment of China (Protection, 1991; Zhao et al., 2017).

2.3. HPEs with rapid rising processes During our campaign, PM1 mass concentration frequently experienced rapid rising, with concentrations doubled at least in several or more hours. To study the different roles of BL meteorology and chemical reactions on these rapid rises in PM1, three persistent heavy polluted episodes (HPEs) with the longest duration and most rapid rise processes were selected, which is HPE1 in early December (from 28th November to 5th December), the red-alert HPE in Mid-December (from 14th to 23rd), and the HPE2 in late December (from 27th December to 9th January) (Fig. 1b, marked with shades) respectively. As our measurements were comprehensive in the red-alert HPE, observations in the other two HPEs were used as additional evidence, and BL meteorological factors, chemical composition, and emission variations were examined in detail in the red-alert HPE. The specific rapid rising processes are marked with shades in Fig. 4.

2.2. QA & QC and data process Different quality assurance and quality control (QA & QC) on the HRToF-AMS, including flow rate calibration, size calibration, mass calibration, and ionization efficiency (IE) calibration, were performed before the campaign. Voltage and peak positions were also calibrated frequently during the whole campaign. The detailed procedures have been described in many previous works (Jayne et al., 2000; Drewnick et al., 2004; Jimenez et al., 2003; J.K. Zhang et al., 2014; Z. Zhang et al., 2017). The raw data of HR-TOF-AMS were processed by AMS data analysis software packages, Squirrel (Version 1.62G) and Pika (Version 1.22G) (http://cires1.colorado.edu/jimenez-group/ToFAMSResources/ ToFSoftware/index.html). Collection efficiency (CE) through the whole campaign was determined based on the relationship between the total mass concentration of PM1 (NR-PM1 + BC) and the calculated PM1 values from the concurrent measurement of TDMPS. The aerosol density used in TDMPS mass calculations was assumed to be that of the average composition of (NH4)2SO4, NH4NO3, and organics, whose densities are 1.77, 1.72, and 1.3 g cm−3, respectively (Y.M. Zhang et al., 2014). The correlation coefficient (R2) is 0.85, demonstrating a good agreement level in the masses obtained by the two methods (Fig. S1). Positive matrix factorization (PMF) was used to resolve organics' mass spectra (Paatero and Tapper, 1994). The organics' matrix and matrix's error between m/z 12 and 200 were processed and generated in Pika package and subsequently evaluated by the PMF Evaluation Tool (PET, v3.05) (Ulbrich et al., 2009). According to specific mass-spectral profiles, temporal mass concentrations, significant

2.4. Multiple linear regression model A multiple linear regression (MLR) model was used to examine meteorological influences on PM1 variability. Six most relevant BL meteorological variables measured in this study were taken into account in this model to represent the BL effect to PM1 to a certain degree, including air temperature, vertical temperature difference, wind speed, RH, air pressure, and solar radiation. To build a robust model, we conducted data exploration and checked our regression model's quality, including linearity, multicollinearity, independence, homoscedasticity, and normality. The scatterplot in Fig. S2 shows that PM1 was basically a linear function of each independent variable, which satisfied the assumption of linearity. A heat map of these meteorological variables shows that no predictors are highly correlated, but there might still be a little 3

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higher than those in other NCP areas. As to the subtypes of OA, three POAs (HOA, BBOA, and CCOA) and two OOAs (LV-OOA and SV-OOA) were resolved by using PMF analysis (Fig. 1c). OOA occupies 47% of OA, while POA contributes 53% of the total organic aerosol. The SVOOA and LV-OOA account for 18% and 29% of OA, respectively. The three components of the POA, including HOA, BBOA, and CCOA, account for 11%, 11%, 31%, respectively. The spectrum of HOA is characterized by prominent hydrocarbon fragment signatures, including unsaturated hydrocarbons C n H+ 2n-1 (m/z: 27, 41, 55, 69) and saturated hydrocarbons CnH+ 2n+1 (m/z: 29, 43, 57, 71) (Fig. 2a), which is similar to previously reported HOA spectra (Sun et al., 2013b; Li et al., 2017; Zhang et al., 2018). The major sources of HOA are vehicle emissions, particularly from diesel trucks and heavy-duty vehicles (Sun et al., 2013b). For diurnal variations, HOA presents a pronounced diurnal cycle with the lowest concentration occurring at midday and the highest concentration at midnight (Fig. 2f). The diurnal cycle of HOA is likely driven by the increasing number of diesel trucks and heavy-duty vehicles at night and diurnal variations in BL structures. The proportion of HOA in total OA (11%) is lower than those observed in Beijing (Sun et al., 2013b; Zhang et al., 2018), which implied fewer contributions from transportation to aerosol pollution in Gucheng. BBOA was resolved with the mass spectrum characterized by the prominent peaks at m/z 60 and m/z 73 (Fig. 2b), two indicative tracers of biomass burning. Analysis of HR OA spectra showed that m/z 60 matched very well with the ion of C2H4O2, which would be the fragment of levoglucosan and related species (Cubison et al., 2011; Hu et al., 2016). The good relationship between m/z 60 and BBOA (R2 = 0.81) further confirmed it (Fig. 2l). It is well known that the primary sources of BBOA are normally from wood combustion, including wildfire and straw combustion such as residues of rice, wheat, and corn. The latter agricultural residue burning might be the major source in consideration of the rural surroundings in Gucheng. BBOA (11%) and HOA (11%) account for roughly equal proportions and thus contribute comparably to air pollution in Gucheng. Coal combustion serves as one of significant energy sources in China and has been widely used as electricity generation, steel milling, cement production, and domestic heating in winter in northern China. A prominent peak at m/z 115 is usually assumed as one specific tracer as CCOA (Zhou et al., 2016). In this study, the signal at m/z 115 in CCOA was also observed (Fig. 2c) and correlated well with CCOA (R2 = 0.83) (Fig. 2m). The proportion of CCOA in total OA (31%) is about twice higher than those of HOA and BBOA, indicating the vital role of coal combustion emissions in aerosol pollution in Gucheng.

multicollinearity (Fig. S3). To further exclude the multicollinearity's disturbance, we used a ridge regression of which the cost function is altered by adding a penalty equivalent to the square of the magnitude of the coefficients, as formula (1): M X

2

^i Þ ¼ ðyi −y

i¼1

M X i¼1

p

yi − ∑ w j  xij j¼0

!2 þλ

p X

w2j

ð1Þ

j¼0

where penalty term (λ = 1.5) regularizes the coefficients to reduce the multicollinearity, y is the PM1 concentrations, x is normalized meteorological variables, and w is regression coefficients. For the rapid rise cases always occur during HPEs, we applied this ridge regression to the redalert HPE as formula (2). y ¼ β0 þ

6 X

ð2Þ

wk xk

k¼1

Histogram, the Q-Q plots, and the residuals for the fitted response values were used to check the consistence of our assumption (Fig. S4). Fig. S4 shows the residuals are basically normalized. This result can also be verified by the Shapiro-Wilk test that has a p value less than 0.05. The residuals variance was basically invariable with response variable magnitude, which indicates that the assumption of homoscedasticity is satisfied. The residuals were also plotted with each of the input variables to check the independence assumption. As shown in Fig. S5, the residuals are basically distributed uniformly and randomly around the zero x-axes and do not form specific clusters, which indicates the assumption holds true. After building this MLR model that satisfied different assumptions, we applied this model to the HPE1 in early December to validate the model's capability. 3. Results and discussion 3.1. General views of PM1 composition and OA sources During our campaign of three consecutive months, the PM1 mass concentration is 191.9 μg m−3 on average and 720.5 μg m−3 at the peak value (Fig. 1b). The major component of PM1 is organics (90.9 μg m−3), accounting for 47% of PM1. Secondary inorganic species are the second component, with its proportion in PM1 reaching up to −3 −3 35%. SO2− (24.8 μg m−3), NO− ), NH+ ), 4 3 (16.6 μg m 3 (14.1 μg m and Cl− (11.7 μg m−3) account for 13%, 9%, 7%, and 6% of PM1, respectively (Fig. 1c). 18% of PM1 is composed of BC, which is significantly

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Fig. 2. Mass spectra (a–e) and diurnal variations (f–j) of HOA, BBOA, CCOA, SV-OOA, and LV-OOA; and time series of OA factors and corresponding tracer compounds (k–o). 4

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meteorological factors and chemical composition in slightly polluted days (SPD) and heavily polluted days (HPD) (Fig. 3). SPD are days with daily PM1 mass concentration less than the 25th percentile of three months, and HPD are days with daily PM1 concentration more than the 75th percentile. The mean PM1 concentration is 85 μg m−3 in SPD and 314 μg m−3 in HPD, respectively. BL meteorological conditions in HPD were strikingly worse compared with those in SPD. Wind speed decreased to less than 1 m s−1, RH increased by ~10%, daily maximum solar radiation decreased by ~100 W m−2, and near-ground temperature inversion (the temperature difference between 30 m and 2 m) increased by 1– 2 °C (Fig. 3a–d) than those in SPD. Under unfavorable meteorological conditions with compressed mixing space, vertically distributed pollutants tended to accumulate in the near-surface atmo− + sphere, and thereby organics, NO− should increase by 3 , NH4 , and Cl equal multiples with their proportions in PM1 changed little. By contrast, the proportion of SO2− increased from 10% in SPD to 15% in 4 HPD, which indicates that aqueous reactions also enhanced with elevated RH in HPD in addition to worsened BL meteorological conditions. Although the proportion of organics in PM1 remained unchanged from LPD to HPD, the proportion of CCOA increased with the decrease in SV-OOA proportion. Less proportion of POA was oxidized to SV-OOA during the near-ground pollution accumulation under diminishing mixing space. For diurnal variations, organics and BC in both SPD and HPD show a striking two-peak mode with the minimum in the afternoon and maximum at night. As the diurnal profile of non-reactive BC is mainly due to the up and down of the BL height, thereby the ratios of chemical species to BC (J. Zhang et al., 2017; Zheng et al., 2015)

The mass spectrum of SV-OOA is characterized by a higher signal at m/z 43 than at m/z 44 (CO+ 2 ), which corresponded to the less oxidized OA components (Fig. 2d). The SV-OOA is likely to be from the oxidation of CCOA due to the similarity between their mass spectra, particularly signals at high m/z. Coal combustion is a significant source of Cl−. The R2 (0.42) of Cl− and the sum of CCOA and LV-OOA is higher than the R2 of Cl− and CCOA (0.31) or LV-OOA (0.2). The better correlation also supported the abovementioned assumption. In that case, organic source from coal combustion become the dominant emission. This is supported by the low value of the OC/BC ratio (2.6), which is similar to that of fossil fuel combustion (2) (Zhang et al., 2015). The mass spectrum of LV-OOA is characterized by prominent CxHyOz fragments, particularly m/z 44 (13.1% of the total LV-OOA signal), which corresponds to more oxidized OA compounds (Fig. 2e). LV-OOA correlates well with NO− 3 (Fig. 2o), for both of them are mainly produced by photochemical reactions (Sun et al., 2013b). The oxidation process to produce LV-OOA was shown by comparing the opposite diurnal variation of SV-OOA and LV-OOA (Fig. 2i, j): from 7:00 to 12:00, contrary to LV-OOA, SV-OOA decreased continuously, which indicates SV-OOA was converted to LV-OOA through complete oxidation processes with sufficiently strong solar radiation; and after 15:00, complete oxidation processes weakened with reduced solar radiation, and SV-OOA began to increase. 3.2. Species and sources in different meteorological conditions To gain an insight into the variations of PM1 composition and organics' sources with pollution aggravation, we further obtained the BL

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Fig. 3. (a–d) Average diurnal profiles of BL meteorological factors, including wind speed (a), RH (b), radiation (c), and temperature differences between 30 m and 2 m (d); (e–f) average PM1 (e) and organic (f) components in slightly polluted days and heavily polluted days, respectively; and average diurnal profiles of carbonaceous species (g–j) (dotted line: SPD, solid line: HPD). 5

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enhanced inversion suppression (Fig. 4b–d). Under high RH, hygroscopic particles were more likely to be deposited at the near-ground layer due to their own mass increments. These findings were also supported by real-time measurements in HPE1 and HPE2 (Figs. S7 and S8). On the second day after the rapid rising, the near surface was heated by increased solar radiation, and thus the vertical temperature difference gradually decreases, and sensible heat and momentum fluxes continue to increase, which increased mixing space. The PM1 that accumulated at the near-ground before diffused vertically and then experienced a reduction at the surface (Figs. 4, S7, S8, and S9). The rise in PM1 can also be associated with accelerated chemical reactions due to RH increases. To estimate the contribution of extraproduced secondary components to PM1 rising, we investigated the diurnal patterns of chemical components' ratios to BC during the red-alert HPE. If we assume that the significant cause of PM1 rising is enhanced by the secondary reaction process, then a significant increase in secondary species should show up during the day-night transition period when the BL effect was excluded. However, almost all ratios of secondary spe− cies to BC, including SO2− 4 , NO3 , and SOA, did not exhibit any ascending trend during the transition period except for a slow increase in Cl−/BC (Fig. 5), which indicates high RH-enhanced aqueous reactions are likely conducive but not dominant with respect to the rise in PM1. The finding was verified by the coexistence of extremely high RH and steep decreases in PM1 in the early morning on 17th, 19th, and 20th (Fig. 4b, d). In contrast, the BL change fairly well explained these steep reductions in PM1, which resulted from the weakened temperature inversion and the enhanced upward momentum flux (Fig. 4b–d). With diurnal effects of BL not considered, the mass size distribution of secondary components increased steadily before the 19th and then remained steady until the end of the HPE (Fig. 6b–e). This indicates that secondary reactions play a more important role in aerosol accumulation on a daily scale instead of a diurnal scale. These reactions are relatively slow processes compared to rapid BL variations in several hours.

were employed to avoid the influence of the boundary layer variation. The results show that the OC/EC was still higher at nighttime than during daytime (Fig. 3i), meanwhile, the CCOA/BC exhibited the most striking peak at midnight among the ratios of POA to BC (Figs. 3j, S6), confirming increase emission from coal combustion at night. In addition, the peak of HOA/BC after midnight (Fig. S2a) suggests the increasing heavy-duty diesel truck activities at night. 3.3. Dominant role of BL meteorology in PM1 rapid rise Fig. 4 gives the temporal variations of chemical species and related meteorological factors during the red-alert HPE episode. This HPE occurred in a horizontally stagnant condition with near-ground wind speed less than 2 m s−1 almost all times and close to 0 m s−1 in many cases (Fig. 4a). This condition is conducive to pollution aggravation and unfavorable to regional transport, showing that horizontal pollutant transport contributed little to PM1 increase. During the days with rapid rising, BL meteorological conditions exhibit pronounced diurnal variations, particularly in surface radiation, sensible heat and momentum fluxes, and vertical temperature differences (Figs. 4 and S9). All the rapid rising processes of PM1 arose at the moment of day-night transition and continued during the nighttime (Fig. 4d, marked with shades), corresponding well with the pronounced deterioration in vertical BL diffusion driven mainly by solar radiation (Fig. 4b, c). As solar radiation reaching the surface diminished during the day-night transitional period (Fig. 4b), the surface layer lost its primary energy source and cooled down, accompanied by a significant decrease in upward sensible heat/momentum fluxes and a rapid increase in the near-ground temperature inversion (the temperature difference between 30 m and 2 m) (Fig. 4b, c). Those decreased upward fluxes resulted in weakened upward heat and material exchange and thus reinforced/maintained near-ground temperature inversion; this inversion inhibited turbulence diffusion upward to weaken/maintain ground-level fluxes in turn (Fig. 4b, c). These joined effects induced a significant reduction of the mixing space in several hours and further resulted in the rapid rise in PM1. The observed rapid synchronous changes in the near-ground inversion, fluxes, and PM1 mass concentration confirm this point (Fig. 4b–d). This rise in PM1 was also facilitated by RH increase resulting from decreased saturated water vapor pressure and

3.4. BL meteorology's impact on the daily-based accumulation In addition to diurnal variations, MLH also exhibited a daily descending trend with the midday MLH decreasing from ~1000 m on 14th to ~250 m after the 18th (Fig. 4d). In heavily polluted days (from 15 to

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Fig. 4. (a) Time series of wind direction and wind speed at Gucheng; (b) time series of solar radiation (SR), clear-sky solar radiation for reference (SR_ref), sensible heat flux (SHFLX), and near-ground temperature differences (δT); (c) time series of RH and momentum flux (MFLX); and (d) time series of mixing layer height (MLH) and PM1 composition during the red-alert HPE. Note: a reference of clear-sky solar radiation was determined from three clean days with minimum PM1 levels in three months (December, January, and February) in winter 2016/17, respectively. 6

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Fig. 5. (a) Diurnal variation profiles in the ratios of PM1 composition to BC; and (b) diurnal variation profiles in the ratios of OA factors to BC during the red-alert HPE. Note: since the ratio of organics to BC is significantly larger than other ratios, we show 1/3 of the ratio in this figure for better display.

process of building and checking the MLR model was elucidated in detail in Section 2.3. Overall, the coefficient of determination (R2) for the MLR model is 0.71 (Figs. 7 and S10), which showed that the variations in BL meteorology accounted for 71% of the variances in PM1 during the red-alert HPE. The fitted model was also used to predict hourly PM1 in the HPE1 with complete observations of the meteorological predictors. The R2 is 0.65, which supports the result that meteorologically driven variability plays a dominant role in PM1 variability. This MLR model with the same meteorological predictors was then applied to chemical composition to explore meteorologically driven variability in 2 different species. The R2 for the MLR model in fitting BC, NH+ 4 , SO4, − and NO3 is above the overall level in fitting PM1 (Fig. 7), indicating more remarkable contributions from BL meteorology to these species.

19 Dec), the midday MLH was reduced by 2/3– 3/4 compared with that in the early stage. As a result, daytime PM1 mass concentration increased continuously day after day. Accumulated PM scattered more solar radiation back to space to substantially reduce surface radiation (Fig. 4b), which lowered the MLH in turn. The mutual promotion of aerosol pollution and unfavorable meteorological conditions has been articulated in previous studies (Zhong et al., 2018a; Zhong et al., 2018b). The meteorologically driven variability in PM1 levels was further explored with a MLR model considering several relevant BL meteorological variables from our field measurements, including air temperature, vertical temperature difference, wind speed, RH, air pressure, and solar radiation. These meteorological predictors closely relate to BL variations and can represent the BL effect to PM1 to a certain degree. The

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(b)

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Fig. 6. Time series of PM1 composition (a) and mass size distributions of secondary inorganic species (b–e) during the red-alert HPE. 7

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attributed to the mutual promotion between unfavorable meteorological conditions and cumulative aerosols via radiation feedback. Overall, 71% of the variances in PM1 can be explained by BL meteorological variability during the red-alert HPE. It is impractical to regulate BL meteorological variability from the mediation perspective, but it may be feasible to intervene with source emissions to reduce PM1 levels. Our results imply that a reduction in coal combustion emissions is a practical approach for remediation of the severe regional aerosol pollution in the NCP. Funding This work was supported by the National Key Research and Development Programs of China (2016YFC0203306) and the National Natural Science Foundation of China (41675121 and 41775121). The data that support the findings of this study are available from the corresponding author upon reasonable request.

Fig. 7. The coefficient of determination (R2) between observations and MLR-fitted results − − + during the red-alert HPE for each PM1 species, including SO2− 4 , NO3 , NH3 , Cl , Organics, and BC.

CRediT authorship contribution statement The R2 for Cl− and organics are lower than that of PM1 (Fig. 7). This might be because current meteorological predictors cannot fully reflect the BL effect or other sources for these two species existed. Comparing real-time chemical composition measurements to BL meteorological contributions from the MLR model shows that Cl- and organics were underestimated at midnight (Fig. S11e, f). Nevertheless, BL meteorology fairly well explained the temporal variation in BC (Fig. S11a), which further verified that the boundary layer effect on air pollutants could be represented by BC change. According to ratios of chemical species to BC (Fig. 5) in this HPE, we found that the CCOA/BC exhibit the most striking peak at midnight, which indicates that the enhanced coal combustion emissions were another source for the rising in organics at midnight, consistent with the previous analysis. This finding was supported by the temporal variations in organic components (Fig. S12). As direct coal combustion emissions are the major sources of Cl− (Sun et al., 2013b), Cl− also increased correspondingly at night.

XZ, YZ, and YW designed the research and led the overall scientific questions. ZZ, XS, JS, SR, and HZ collected AMS and TDMPS data. ZG and YL provided meteorological data. YZ and JZ carried out data processing and analysis. KG and LZ helped with data processing. JZ wrote the first draft of the manuscript and then XZ and YZ revised the manuscript. All authors read and approved the final version. Declaration of competing interest The authors declare no competing financial interests. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2020.143394. References

4. Conclusion

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We have identified PM1 chemical species, OA sources, and the different roles of BL meteorology and secondary reactions in the rapid rise and daily-based accumulation of PM1 by conducting three-month comprehensive field measurements in Gucheng. Our results demonstrated OA sources were comprised of three POA factors associated with traffic (HOA), biomass burning (BBOA), and coal combustion (CCOA), and two OOA factors with different oxidation degrees (SV-OOA and LV-OOA). SV-OOA was likely from the preliminary oxidation of CCOA, indicating coal combustion was the most important source of OA; LV-OOA was the photochemical products with SV-OOA involved. The emission sources of OA exhibited a day-night disparity with an increase primarily in coal combustion in the nighttime. Our findings also revealed the diurnal and daily BL meteorological variability played a leading role in the rapid rise of PM1 in several hours and daily-based PM1 accumulation, respectively. The rapid rise in PM1 was mainly due to the substantial reduction in mixing space resulting from a significant increase in the near-ground temperature inversion and significant decreases in upward sensitive heat and momentum fluxes during the day-night transition period with diminishing solar radiation. A partial rise in OA was attributed to coal combustion enhancements at midnight. Although high RH enhanced aqueous reactions during the rising processes, extra-produced secondary components were likely conducive but not dominant with respect to the rise in PM1. In contrast, secondary reactions contributed more to the daily-based PM1 accumulation. The daily-based accumulation of PM1 was controlled by the day-to-day worsening in mixing space, which decreased by 2/3– 3/4 in heavily polluted days if described by daytime MLH. This worsening can be partly 8

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