Characteristics of Volatile Organic Compounds in Nanjing and Suzhou, Two Urban Sites in the Yangtze River Delta, China.

A field measurement study of volatile organic compounds (VOCs) was performed in January 2015 in the urban areas of two of the most important cities of the Yangtze River Delta: Nanjing and Suzhou. The objectives of this study included comparing the characteristics of VOC concentrations, comparing the impacts of emissions on VOCs, using species ratios to assess air mass age, and evaluating ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAP) in air masses. The VOC concentrations in Nanjing (34.6 ± 5.8 ppbv) were higher than those of Suzhou (28.1 ± 5.6 ppbv). The most abundant VOC measured was ethane (6.6 ppbv in Nanjing and 3.6 ppbv in Suzhou). Relevant analysis shows that motor vehicle emissions in Suzhou were dominant, whereas industrial emissions in Nanjing also contributed to VOCs. During rush hour, the VOC concentrations in Nanjing were the highest (35.3 ppbv). The T/B ratio (0.92-1.79) for the two sites was observed to be relatively low in the other studied cities, indicating the source impact of traffic emissions in the two sites. Indicators X/B (0.26-0.39) and X/E (0.33-0.66) also confirmed an aging air mass was transported at the two sampling sites. According to principal component factor analysis results, vehicle emissions (44.8% in Nanjing and 30.6% in Suzhou) were the most important contribution to the two sites. Industrial sites were not only likely to experience industrial emissions but were affected by traffic emissions. Using the OFP method, both sites showed the largest percentage of alkenes (59.9% in Nanjing and 62.0% in Suzhou). When comparing SOAP, both sites showed an absolute majority of aromatics (97.6% in Nanjing and 98.3% in Suzhou). To control the formation of O3 and SOA in the two sites, it is necessary to reduce the concentration of alkenes and aromatics, respectively. By CPF analysis, pollutants transported from the SE and NE have significant effects on the Nanjing site. In the Suzhou, roads and industrial parks in the SE and S of Suzhou have significant impacts on the site.

Evolution of boundary layer ozone in Shijiazhuang, a suburban site on the North China Plain.

The structure of the boundary layer affects the evolution of ozone (O3), and research into this structure will provide important insights for understanding photochemical pollution. In this study, we conducted a one-month observation (from June 15 to July 14, 2016) of the boundary layer meteorological factors as well as O3 and its precursors in Luancheng County, Shijiazhuang (37°53'N, 114°38'E). Our research showed that photochemical pollution in Shijiazhuang is serious, and the mean hourly maximum and mean 8-hr maximum O3 concentrations are 97.9 ±â€¯26.1 and 84.4 ±â€¯22.4 ppbV, respectively. Meteorological factors play a significant role in the formation of O3. High temperatures and southeasterly winds lead to elevated O3 values, and at moderate relative humidity (40%-50%) and medium boundary layer heights (1200-1500 m), O3 production sensitivity occurred in the transitional region between volatile organic compounds (VOC) and nitrogen oxides (NOx) limitations, and the O3 concentration was the highest. The vertical profiles of O3 were also measured by a tethered balloon. The results showed that a large amount of O3 was stored in the residual layer, and the concentration was positively correlated with the O3 concentration measured the previous day. During the daytime of the following day, the contribution of O3 stored in the residual layer to the boundary layer reached 27% ±â€¯7% on average.

Seasonal Variation in Water-Soluble Ions in Airborne Particulate Deposition in the Suburban Nanjing Area, Yangtze River Delta, China, During Haze Days and Normal Days.

To investigate the seasonal variation and characterization of water-soluble ions (WSIs) present in airborne particle deposition (APD) during Haze Days (visibility ≤7.5 km) and Normal Days (visibility >7.5 km) in suburban Nanjing area, 151 filter samples were collected from 18 May 2013 to 26 May 2014. Ten different WSIs from the samples were determined by Ion Chromatography. The results indicated that secondary WSIs (NH4+, NO3-, and SO42-) were the main ions in the WSIs, averaging 17.2, 18.5, and 17.1 µg/m3, respectively, and accounting respectively 20.9, 22.5, and 20.8% of the total WSIs. On Haze Days, the concentration of WSIs increased dramatically in fine size (particle size <2.1 µm), especially for NH4+, NO3-, and SO42- (increased by 52.6, 71.3, and 73.1%, respectively), whereas the concentrations of WSIs increased slowly in coarse size (2.1 µm < particle size < 10 µm), in which NH4+, NO3-, and SO42- increased by 14.7, 27.2, and 54.5%, respectively. According to the backward trajectories and the principal component analysis analysis, Nanjing APD were mainly derived from the soil dust in northern China (35%) in the spring, from ocean air masses (61 and 55%) in the summer and the autumn, and from local air masses (73%) in the winter. On summer Haze Days, secondary components in PM2.1 consisted mainly of (NH4)2SO4 and NH4NO3, whereas secondary components in PM2.1-10 consisted mainly of (NH4)2SO4, NH4Cl, and NH4NO3. The increasing concentrations of secondary components increase the light extinction coefficients of aerosol on winter and autumn Haze Days. The concentrations of WSIs in fine size rose sharply on Haze Days, leading the visibility to exponential decline. Differently, the concentrations of WSIs in coarse size were not the main cause in the change of the visibility.

Source Apportionment of Volatile Organic Compounds in an Urban Environment at the Yangtze River Delta, China.

Volatile organic compounds (VOCs) were collected continuously during June-August 2013 and December 2013-February 2014 at an urban site in Nanjing in the Yangtze River Delta. The positive matrix factorization receptor model was used to analyse the sources of VOCs in different seasons. Eight and seven sources were identified in summer and winter, respectively. In summer and winter, the dominant sources of VOCs were vehicular emissions, liquefied petroleum gas/natural gas (LPG/NG) usage, solvent usage, biomass/biofuel burning, and industrial production. In summer, vehicular emissions made the most significant contribution to ambient VOCs (38%), followed by LPG/NG usage (20%), solvent usage (19%), biomass/biofuel burning (13%), and industrial production (10%). In winter, LPG/NG usage accounted for 36% of ambient VOCs, whereas vehicular emissions, biomass/biofuel burning, industrial production and solvent usage contributed 30, 18, 9, and 6%, respectively. The contribution of LPG/NG usage in winter was approximately four times that in summer, whereas the contribution from biomass/biofuel burning in winter was more than twice that in summer. The sources related to vehicular emissions and LPG/NG usages were important. Using conditional probability function analysis, the VOC sources were mainly associated with easterly, northeasterly and southeasterly directions, pointing towards the major expressway and industrial area. Using the propylene-equivalent method, paint and varnish (23%) was the highest source of VOCs in summer and biomass/biofuel burning (36%) in winter. Using the ozone formation potential method, the most important source was biomass/biofuel burning (32% in summer and 47% in winter). The result suggests that the biomass/biofuel burning and paint and varnish play important roles in controlling ozone chemical formation in Nanjing.

Temporal Variations of O3 and NO x in the Urban Background Atmosphere of Nanjing, East China.

Rapid economic growth has given rise to a significant increase in ozone (O3)-precursor emissions in many regions of China. An improved understanding of O3 formation in response to different precursor emissions is imperative to address the highly nonlinear O3 problem and to provide a solid scientific basis for efficient O3 abatement in these regions. To this end, this study was performed in Nanjing using a set of observational data from June 1, 2013, to May 31, 2014. The results showed that O3 concentrations were positively correlated with wind speed and temperature and were significantly negatively correlated with relative humidity. The highest monthly daytime, nighttime, and daily average O3 concentrations were observed in summer with values of approximately 46, 18 and 30 ppb, respectively. The lowest O3 concentrations were observed in November through January with values as low as 17, 4, and 9 ppb for the daytime, nighttime, and daily concentrations, respectively. The highest daytime average NO and NO2 concentrations were observed in December, whereas the lowest concentrations were observed in July. A unimodal O3 peak was observed with the highest O3 levels in summer followed by spring and then autumn; the lowest levels observed in the winter. The O3 concentration reached maximum levels at 14:00 to 15:00 h (local standard time). It was found that the crossover occurred with approximately several hours difference with the earliest occurring in summer (06:00 h) followed by spring (08:00 h), autumn (09:00 h), and winter (10:30 h). Furthermore, the highest constant rate of O3 accumulation was observed in summer (5.6 ppb/h) followed by autumn (4.8 ppb/h), spring (4.5 ppb/h), and winter (2.7 ppb/h). The oxidant intercept ranged from 28.4 ppb in January to 58.6 ppb in June, although the slope also shows substantial variation from 0.18 in June to 0.67 in August. The weekend effect is stronger in spring and summer than in autumn and winter and is more intense on Sundays than on Saturdays. Thus, the decrease of O3 levels during weekends suggests that it may be NO x -sensitive.

Fine particulate pollution in the Nanjing northern suburb during summer: composition and sources.

To understand the chemical composition characteristic of pollution in a northern suburb of Nanjing, particle samples were collected by two Andersen cascade impactors from May to July 2013. The positive matrix factorization version 3 (EPA-PMF 3.0) was applied to identify the source contribution of PM2.1 concentrations in the study area. Source categories were determined based on the chemical component abundances in the source profiles. Overall, results indicated that seven factors were obtained. The factors are identified as follows: (I) secondary aerosol, characterized by high concentrations of NH4 (+), NO3 (-), and SO4 (2-), accounting for 20.22 %; (II) metallurgical aerosol, characterized by high concentrations of Pb, Cd, and Zn, accounting for 6.71 %; (III) road dust, characterized by high concentrations of Mg, Ca, Na, Al, and Ba, accounting for 11.85 %; (IV) biomass burning, characterized by high concentrations of K(+), Na(+), Cl(-), and K, accounting for 10.17 %; (V) residual oil, characterized by high concentrations of V and Cr, accounting for 16.63 %; (VI) iron and steel industry, characterized by high concentrations of Mn and Fe, accounting for 9.48 %; and (VII) vehicle exhaust, characterized by high concentrations of organic carbon (OC), Mo, elemental carbon (EC) and K, accounting for 24.94 %.

[Predictive Model for O3 in Shanghai Based on the KZ Filtering Technique and LSTM].

In this study, a Kolmogorov-Zurbenko （KZ） filter was proposed to decompose the original ozone （O3） sequence to improve the accuracy of ozone long-term series prediction and select relevant meteorological features. Furthermore, the enhanced maximal minimal redundancy （mRMR） feature selection technique was combined with the support vector regression （SVR） approach to select the most illuminating meteorological features. Subsequently, from May to August 2023, during high ozone concentration periods, a long short-term memory network （LSTM） was utilized to assess and predict high ozone concentration periods at the monitoring stations of Jingan （urban area）, Pudong-Chuansha （suburban area）, and Dianshan Lake （suburban area） in Shanghai. The results showed that pressure, temperature, humidity, boundary layer height, and wind direction were the best combinations of O3 baseline and short-term components, as chosen by feature screening. The R2 values for Jingan Station, Pudong-Chuansha Station, and Dianshan Lake Station were 0.86, 0.83, and 0.85, respectively. The RMSE values were 18.26, 18.74, and 20.02 µg·m-3, respectively. These findings suggest that decomposing the original O3 sequence improved the prediction accuracy of ozone concentrations. Additionally, as indicated by the R2 and RMSE values found for every monitoring station, feature screening preserved the model's predictive performance.

[Vertical Distribution Characteristics of Boundary Layer Volatile Organic Compounds in Autumn in the Mixed Industrial and Rural Areas over the Northern Suburb of Nanjing].

Based on the sounding data of VOCs in the lower troposphere (0-1000 m) in the northern suburb of Nanjing in the autumn of 2020, the vertical profile distribution, diurnal variation, and photochemical reactivity of VOCs in this area were analyzed. The results showed that the volume fraction of VOCs decreased with the increase in height (72.1×10-9±28.1×10-9-56.4×10-9±24.8×10-9). Alkanes at all heights accounted for the largest proportion (68%-75%), followed by aromatics (10%-12%), halohydrocarbons (10%-11%), alkenes (3%-7%), and acetylene (2%). The diurnal variation of the boundary layer had a great influence on the VOCs profile. The lower boundary layer in the morning and evening caused the volume fraction of VOCs to accumulate near the ground and lower in the upper layer. The vertical distribution of VOCs was more uniform in the afternoon. In the morning, the volume fraction proportion of alkenes (alkanes) with strong (weak) photochemical reactivity decreased (increased) with the increase in height, indicating that the photochemical aging of VOCs in the upper layer was significant. In the afternoon, the vertical distribution of VOCs volume fraction and OFP in the lower troposphere were more uniform. Affected by the surrounding air masses with different sources, the volume fraction and component proportion of VOCs at each height were significantly different. The alkanes in rural air masses were vertically evenly distributed, and the proportion increased gradually with the height. The vertical negative gradient of VOCs volume fraction in the urban air mass was the largest, the volume fraction of VOCs near the ground was high, and it was rich in aromatics. The proportion of aromatics increased with the increase in VOCs volume fraction between 200-400 m height of industrial air mass. The near-surface VOCs volume fraction of the highway traffic air mass was high, and alkanes accounted for the largest proportion.

[Atmospheric Ozone Concentration Prediction in Nanjing Based on LightGBM].

Based on the air quality data and conventional meteorological data of the Nanjing Region from January 2015 to December 2016, to analyze the characteristics of O3 concentration changes in the Nanjing Region, a light gradient boosting machine (LightGBM) model was established to predict O3 concentration. The model was compared with three machine learning methods that are commonly used in air quality prediction, including support vector machine, recurrent neural network, and random forest methods, to verify its effectiveness and feasibility. Finally, the performance of the prediction model was analyzed under different meteorological conditions. The results showed that the variation in O3 concentration in Nanjing had significant seasonal differences and was affected by a combination of its pre-concentration, meteorological factors, and other air pollutant concentrations. The LightGBM model predicted the ground-level O3 concentration in the Nanjing area more precisely to a large extent (R2=0.92), and the model outperformed other models in prediction accuracy and computational efficiency. In particular, the model showed a significantly higher prediction accuracy and stability than that of other models under a high-temperature condition that was more likely prone to ozone pollution. The LightGBM model was characterized by its high prediction accuracy, good stability, satisfactory generalization ability, and short operation time, which broaden its application prospect in O3 concentration prediction.

Characterizations of volatile organic compounds during high ozone episodes in Beijing, China.

Air samples were collected in Beijing from June through August 2008, and concentrations of volatile organic compounds (VOCs) in those samples are here discussed. This sampling was performed to increase understanding of the distributions of their compositions, illustrate the overall characteristics of different classes of VOCs, assess the ages of air masses, and apportion sources of VOCs using principal compound analysis/absolute principal component scores (PCA/APCS). During the sampling periods, the relative abundance of the four classes of VOCs as determined by the concentration-based method was different from that determined by the reactivity approach. Alkanes were found to be most abundant (44.3-50.1%) by the concentration-based method, but aromatic compounds were most abundant (38.2-44.5%) by the reactivity approach. Aromatics and alkenes contributed most (73-84%) to the ozone formation potential. Toluene was the most abundant compound (11.8-12.7%) during every sampling period. When the maximum incremental reactivity approach was used, propene, toluene, m,p-xylene, 1-butene, and 1,2,4-trimethylbenzene were the five most abundant compounds during two sampling periods. X/B, T/B, and E/B ratios in this study were lower than those found in other cities, possibly due to the aging of the air mass at this site. Four components were extracted from application of PCA to the data. It was found that the contribution of vehicle exhaust to total VOCs accounted for 53% of VOCs, while emissions due to the solvent use contributed 33% of the total VOCs. Industrial sources contributed 3% and biogenic sources contributed 11%. The results showed that vehicle exhausts (i.e., unburned vehicle emissions + vehicle internal engine combustion) were dominant in VOC emissions during the experimental period. The solvent use made the second most significant contribution to ambient VOCs.

[Characteristics and Health Risk Assessment of BTESX in the Northern Suburbs of Nanjing].

AMA GC5000BTX was used to monitor the mixing ratio of benzene, toluene, ethylbenzene, m,p-xylene, o-xylene, and styrene (BTESX) in the atmosphere of the northern suburb of Nanjing from January 2014 to December 2016. The temporal variation characteristics of BTESX and the influence of meteorological elements on it were analyzed, and the characteristic ratio method (T/B) was used to qualitatively analyze the source of BTESX. Finally, the human exposure analysis and evaluation method of EPA was used to evaluate the health risk of BTESX. The results showed that during the observation period, the average mixing ratio of BTESX was (7.28±6.63)×10-9, and the mixing ratio of benzene was the highest at (2.45±3.91)×10-9. The mixing ratio of other species from large to small was toluene>ethylbenzene>m,p-xylene>o-xylene>styrene, which were (2.41±2.61)×10-9, (1.37±1.28)×10-9, (0.51±0.48)×10-9, (0.3±0.36)×10-9, and (0.22±0.42)×10-9, respectively. Due to the existence of stable aromatic sources, the monthly and seasonal variation in BTESX mixing ratio was not as obvious as that of other species (NOx, CO, SO2, PM2.5, etc.). The weekend effect of BTESX and other pollutants was not significant. The mixing ratio of BTESX was largely affected by the short distance transportation of chemical enterprises and traffic trunk roads in the northeast, resulting in a large mixing ratio of BTESX in the northeast. The mixing ratio of BTESX was jointly affected by relative humidity and temperature, and its high value area was mainly located in the range of 30%-70% relative humidity. In this range of relative humidity, the high value range of BTESX volume fraction increased with the elevation of temperature. The HI (hazard index) of BTESX in different seasons was within the safety range recognized by EPA, whereas the R (carcinogenic risk of benzene) value was higher than the safety threshold specified by EPA. At the same time, the HI and R values were higher in summer, to which great attention should be paid.

Reconciling the bottom-up methodology and ground measurement constraints to improve the city-scale NMVOCs emission inventory: A case study of Nanjing, China.

Reliable emission estimate of non-methane volatile organic compounds (NMVOCs) is important for understanding the atmospheric chemistry and formulating control policy of ozone (O3). In this study, a speciated emission inventory of anthropogenic NMVOCs was developed with the refined "bottom-up" methodology and best available information of individual sources for Nanjing in 2017. The total NMVOCs emissions were calculated at 163.2 Gg. It was broken down into the emissions of over 500 individual species and aromatics took the largest fraction (33.3% of the total emissions). Meanwhile, 105 compounds were measured at 5 sites representing different functional zones of Nanjing for one year. The annual mean concentration of totally 105 species varied from 48.5 ppbv to 63.7 ppbv, and alkanes was the most abundant group with its mass fractions ranging 37.2-40.1% at different sites. Constrained by the emission ratios of individual species versus carbon monoxide (CO) based on ground measurement, the total emissions of 105 species (NMVOCs-105) was estimated at 195.6 Gg, 81.1% larger than the bottom-up estimate of NMVOCs-105 (108.0 Gg). The constrained emissions indicated an overestimation of aromatics and underestimation of OVOCs and halocarbons in the bottom-up emission inventory because of the uncertainties in source profiles. O3 simulation with Community Multi-scale Air Quality (CMAQ) model was conducted for January, April, July and October in 2017 to evaluate the bottom-up and constrained emission estimates. The mean normal bias (MNB) and mean normal error (MNE) values were generally within the criteria (MNB ≤ ±15% and MNE ≤ 30%) for both inventories. The model performance was improved when the constrained estimates were applied, indicating the benefit of ground observation constraints on NMVOCs emission estimation and O3 simulation. Based on the O3 formation potential (OFP), 12 key NMVOCs species mainly from surface coating, on-road vehicles and oil exploitation and refinery were identified as the priority compounds for O3 reduction.

[Size Distribution of Particulate Chemical Components in Nanjing Jiangbei New Area].

In order to explore the pollution characteristics of the chemical components of atmospheric particulate matter in Nanjing Jiangbei New Area, size-resolved samples were collected from 2013 to 2014. The size distribution and seasonal variation of the chemical components of the particles were studied. The results showed that the total concentration of nine water-soluble ions in fine particles was higher in autumn and winter than in spring and summer, while the concentration of coarse particles was highest in winter. The ratio of NO3-/SO42- for the fine particles in winter was slightly higher than in the other three seasons, and lowest in the coarse particles. The trend of particle size distribution in spring, summer and autumn was consistent. The distribution of water-soluble ions was bimodal, and NO3- peaked at 0.65-1.1 µm in all four seasons. The peak radius of SO42- in the fine particles in summer and autumn was 0.43-0.65 µm, and the peak value in winter moved towards the coarser particles, while Na+ and Cl- mainly existed in the coarse particles. The charge equivalent of anion and anion indicated that the coarse and fine particles were alkaline and weakly alkaline, respectively. Organic carbon (OC) and elemental carbon (EC) mainly existed in the fine particles, with a bimodal distribution. The secondary organic carbon (SOC) in the fine particles in autumn and winter was significantly higher than in spring and summer. The ratio method further indicated that the carbonaceous component of the particulate matter in Nanjing Jiangbei New Area is mainly from the emissions of coal and biomass combustion, and vehicle exhaust.

High-resolution anthropogenic ammonia emission inventory for the Yangtze River Delta, China.

The Yangtze River Delta (YRD) is one of the regions with air pollution and high ammonia (NH3) emission in China. A high-resolution ammonia emission inventory for the YRD region was developed based on the updated source-specific emission factor (EFs) and the county-level activity data. The 1 × 1 km gridded emissions were allocated by using the appropriate spatial surrogate. The total NH3 emissions changed insignificantly from 2006 to 2014 and varied in the range of 981.65 kt - 1014.30 kt. The fertilizer application and livestock were the major contributors of total emission. Humans, biomass burning and vehicles were the top three contributors of non-agricultural sources, accounting for 37.24%, 31.02% and 10.85%, respectively. Vehicles were calculated to be the non-agricultural source with the fastest annual growth rate. NH3 emissions from the nitrogen fertilizer application generally peaked in summer, corresponding to the planting schedule and relatively high temperature. High NH3 emissions occurred in the north as opposed to low emissions in the south of the YRD. The cities of Xuzhou, Yancheng and Nantong with more agricultural activities were demonstrated to have relatively high NH3 emissions, contributing 10.0%, 9.0 and 7.1% of total emissions, respectively. The validity of the emission estimates was further evaluated based on the uncertainty analysis by Monte Carlo simulation, comparison with previous studies, and correlation analysis between NH3 emission density and observed ground NH3 concentration. A detailed NH3 emission inventory is the basis of regional-scale air quality model simulation and can provide valuable information for understanding the formation mechanism of pollutants.

Spatial and seasonal characteristics of particulate matter and gaseous pollution in China: Implications for control policy.

By employing the air pollution data including particular matter (PM) and gaseous pollutants (SO2, NO2, CO, and O3) measured over 130 cities in China from April 2014 to March 2015, the spatial and seasonal variations of air pollution are analyzed. The 9 representative regions including Beijing, Tianjin, and Hebei (BTH), Yangze River Delta (YRD), central China (CC), Sichuan Basin (SB), northeast China (NEC), northwest China (NWC), Pearl River Delta (PRD), Yungui Plateau (YP), and Tibet, are chose to quantify the conditions of PM and gaseous pollution. According to the magnitudes of PM2.5 from high to low, the regions are listed in sequence as BTH, CC, SB, YRD, NEC, NWC, PRD, YP, and Tibet. The spatial patters of gaseous pollutants except O3 are generally consistent with PM's. The CO maximum is found in BTH and NWC while the O3 maximum in YRD, PRD, and Tibet. The seasonal cycles of SO2 and NO2 are quite similar to that of PM, but the SO2 is overall higher than NO2 in winter over the northern China while the opposite is true over the southern China. The O3 concentrations are generally low in winter, but high in spring and summer due to active photochemical reaction when temperature is high. The percentage of haze days (daily PM2.5 exceeds NAAQS Grade II, i.e. 75  µg m-3) to the entire year is 45, 32 and 29%, respectively over BTH, CC, and SB, three most PM pollution regions during the study period. Although the most severe haze region occurs in BTH (139 days) from annual mean, the most severe winter in SB (54 days) owing to its basin landform and high air pollutant emissions. In contrary to PM pollution, gaseous pollution in China are overall quite trivial.

PM2.5 vertical variation during a fog episode in a rural area of the Yangtze River Delta, China.

A dense radiation fog event occurred at the Shouxian site, Anhui Province, China, from the evening of January 2 to noon on January 3, 2017. During this event, vertical profiles of particulate matter (PM) and meteorological parameters within the lower troposphere (0-1000 m) were collected using a tethered balloon. This study assessed the evolution of the PM2.5 profile with the planetary boundary layer (PBL) structure and the effects of fog on the PM2.5 concentration. The results showed the following: (1) At the surface, the average diurnal variation in Aitken mode, accumulation mode and coarse mode particles had bimodal patterns before fog formation and was mainly influenced by diurnal variation in the mixing level depth (MLD). The aerosol number concentrations decreased remarkably, and the PM2.5 was strongly scavenged from 150 µg/m3 to 45 µg/m3 during the fog process. (2) In the vertical direction, the PM2.5 distribution was affected by the PBL height and the vertical fog structure. At 05:00 LT (local time) (i.e., early morning before the fog event), the PM2.5 concentration was slightly higher in the stable layer (260 µg/m3) than in the residual layer (200 µg/m3). At 14:00 LT (haze period), PM2.5 was well mixed below 500 m, with a concentration of 310 µg/m3. After 20:00 LT, when fog formed, PM2.5 was scavenged from the surface to the upper layers, and the scavenging height was controlled by the fog top height. (3) The vertical development of fog was promoted by turbulent mixing and radiation cooling at the fog top. Turbulent mixing enhanced the particle scavenging efficiency of fog droplets by the collision-coalescence process. The PM2.5 scavenging height was corresponded to the turbulence height. Therefore, turbulence development in the fog was the essential dynamic factor driving PM2.5 reduction.

Vertical distributions of black carbon aerosols over rural areas of the Yangtze River Delta in winter.

Based on a field campaign in Shouxian, a rural site on the Yangtze River Delta, China, from December 14, 2016 to January 4, 2017, the vertical profiles of black carbon (BC) and planetary boundary layer (PBL) structures were studied. In total, 58 vertical profiles were obtained, including of the PM2.5, BC mass concentration (mBC) and relevant meteorological parameters. Four profile types were categorized: I: uniform vertical distributions (38%), II: higher values at lower altitudes (29%), III: bimodal distributions with high values near the ground and at higher altitudes (17%), and IV: unimodal distributions with high values at higher altitudes (11%). A further analysis confirmed that all types were mainly influenced by the PBL diurnal evolution and local emissions, while types III and IV were strongly associated with the temperature inversions at low altitudes. The diurnal variations of the BC vertical profiles mainly followed the evolution of the PBL. In the early morning, the average mBC within the PBL (MBL, BC) increased significantly, reaching the highest level in the diurnal cycles, i.e., approximately 13.0 µg m-3. The pollutants were confined to a thin layer <0.2 km above the ground, which contributed to the smoke produced by local residential biomass burning. Around noon, the accumulated BC in the layer was diluted as a result of the development of the PBL. The height of the PBL (HPBL) reached its maximum in the afternoon, with an average of 0.65 km, while MBL, BC dropped to its minimum, with an average of 7.8 µg m-3. As evening approached, the BC produced by local residential biomass burning gradually accumulated near the ground and linearly declined along the standardized height (HS) within the nocturnal boundary layer (NBL). There were large differences in the BC concentration within and above the PBL both in the daytime and at night.

[Application of Support Vector Machine Regression in Ozone Forecasting].

Support vector machine regression (SVMr) was proposed to forecast hourly ozone (O3) concentrations, daily maximum O3 concentrations, and maximum 8 h moving average O3 concentrations (O3 8 h) by employing the observations of meteorological variables and O3 and its precursors during the high O3 periods from May 20 to August 15, 2016 at an industrial area in Nanjing. The squared correlation coefficient (R2) of the hourly O3 concentrations forecast was 0.84. The mean absolute error (MAE) and mean absolute percentage error (MAPE) were 3.44×10-9 and 24.48, respectively. The key factors for the hourly O3 forecast were the O3 pre-concentrations, amount of ultraviolet radiation B (UVB), and the NO2 concentration. The main factors for the O3 daily maximum forecast were the NOx concentrations at 07:00 and the UVB level. Temperature and UVB played an important role in predicting O3 8 h. In general, taking precursors into account could increase the accuracy of O3 prediction by 10%-28%. For O3 concentration forecasting, SVMr gave significantly better predictions than multiple linear regression methods.

[Urban Aerosol Hygroscopicity During Haze Weather].

The hygroscopicity of aerosols has an important influence on atmospheric visibility and is one of the main causes of haze pollution. Based on observations of the aerosol hygroscopic growth factor (GF), water soluble inorganic ions, and organic carbon/elemental carbon (OC/EC) data during haze weather from April 17 to May 21, in 2014, the hygroscopic properties of aerosols and corresponding effects on haze in Nanjing were analyzed. The results showed that the distribution of GF was bimodal and varied from 1.12 to 1.64. With the increase of particle size, the average hygroscopic growth factor (GFmean) changed less and the standard deviation of wettability (σ) increased gradually; meanwhile, the degree of external mixing of chemical components increased gradually. The hygroscopicity of aerosol particles in the day was better than that at night, but the mixing degree was weaker than that at night; in non-haze weather, the hygroscopicity of aerosol particles was stronger and the degree of external mixing was higher, while the hygroscopicity and mixing degree of haze particles showed opposite trends. With the increase of haze levels, the hygroscopicity of aerosol particles grew weaker and the degree of external mixing decreased further. Relative humidity can have a significant impact on the chemical components of aerosols and their hygroscopic capacity. Under a low humidity background, the main chemical components of aerosols included NH4+, NO3-, SO42-, OC, and EC, and the content of OC/EC in aerosols during haze days was more abundant; in haze weather with low relative humidity, abundant organic matter was the main reason for the decrease of the moisture absorption capacity of small-scale aerosols. The level of relative humidity in the haze weather was also an important factor affecting the hygroscopic capacity of aerosols. The contents of (NH4)2SO4, OC, and insoluble substances in aerosols were the highest, followed by NH4NO3. The contents of these chemical components showed obvious diurnal variation characteristics, which resulted in significant diurnal variation of the hygroscopicity of the aerosols. κchem calculated by the chemical composition and κmean acquired by observations using H-TDMA showed good consistency, and the correlation coefficient was 0.8903. In haze weather, the correlation between them was further enhanced. Therefore, the major chemical components of aerosols could be used to predict the hygroscopic properties of aerosols.

[Source Analysis of Volatile Organic Compounds in the Nanjing Industrial Area and Evaluation of Their Contribution to Ozone].

Ambient volatile organic compounds (VOCs) were continuously measured during the high ozone (O3) periods from May 1 to May 31 and June 1 to July 16, 2015 at an industrial area in the north suburb of Nanjing. A positive matrix factorization (PMF) model and an observation-based model (OBM) were combined for the first time to investigate the contributions of VOC sources and species to local photochemical O3 formation. The average VOC concentrations in 2014 and 2015 were (36.47±33.44)×10-9 and (34.69±34.08)×10-9, respectively. The VOC sources identified by the PMF model for 2014 and 2015 belonged to 7 source categories, including vehicular emissions, liquefied petroleum gas usage, biogenic emissions, furniture manufacturing industry, chemical industry, chemical coating industry, and chemical materials industry emission sources. The OBM was modified to assess the O3 precursors' relationships. Generally, photochemical O3 production was VOC limited, with positive relative incremental reactivity (RIR) values for VOC species and a negative RIR value for NO. It can be seen that alkenes (1.20-1.79) and aromatics (1.42-1.48) presented higher RIR values and controlling O3 would be the most effective when the VOC emissions from alkenes were reduced by 80%. Vehicle emissions (1.01-1.11), LPG (0.74-0.82), biogenic emissions (0.34-0.42), and furniture manufacturing industry (0.32-0.49) sources were the top four VOC sources making significant contributions to photochemical O3 formation, which suggests that controlling vehicle emissions, biogenic emissions, LPG, and furniture manufacturing industry sources should be the most effective strategy to reduce photochemical O3 formation.