As Volatile Organic Compounds (VOCs) are one of the precursors of ozone, their distribution and variable concentrations are highly related to local ozone pollution control. In this study, we obtained ...vertical profiles of VOCs in Shanghai’s Jinshan district on 8 September and 9 September in 2016 to investigate their distribution and impact on local atmospheric oxidation in the near surface layer. Vertical samples were collected from heights between 50 m and 400 m by summa canisters using an unmanned aerial vehicle (UAV). Concentrations of VOCs (VOCs refers to the 52 species measured in this study) varied minimally below 200 m, and decreased by 21.2% from 100 m to 400 m. The concentrations of VOCs above 200 m decreased significantly in comparison to those below 200 m. The proportions of alkanes and aromatics increased from 55.2% and 30.5% to 57.3% and 33.0%, respectively. Additionally, the proportion of alkenes decreased from 13.2% to 8.4%. Toluene and
m/p
-xylene were the key species in the formation of SOA and ozone. Principal component analysis (PCA) revealed that the VOCs measured in this study mainly originated from industrial emissions.
As a kind of metabolically triggered inflammation, obesity influences the interplay between the central nervous system and the enteral environment. The present study showed that β-elemene, which is ...contained in various plant substances, had effects on recovering the changes in metabolites occurring in high-fat diet (HFD)-induced obese C57BL/6 male mice brains, especially in the prefrontal cortex (PFC) and hippocampus (HIP). β-elemene also partially reversed HFD-induced changes in the composition and contents of mouse gut bacteria. Furthermore, we evaluated the interaction between cerebral metabolites and intestinal microbiota via Pearson correlations. The prediction results suggested that Firmicutes were possibly controlled by neuron integrity, cerebral inflammation, and neurotransmitters, and Bacteroidetes in mouse intestines might be related to cerebral aerobic respiration and the glucose cycle. Such results also implied that Actinobacteria probably affected cerebral energy metabolism. These findings suggested that β-elemene has regulatory effects on the imbalanced microbiota-gut-brain axis caused by obesity and, therefore, would contribute to the future study in on the interplay between cerebral metabolites from different brain regions and the intestinal microbiota of mice.
The EXPeriment on the eLucidation of the atmospheric Oxidation capacity and aerosol foRmation and their Effects in the Yangtze River Delta (EXPLORE-YRD) campaign was carried out between May and June ...2018 at a regional site in Taizhou, China. The EXPLORE-YRD campaign helped construct a detailed air quality model to understand the formation of O3 and PM2.5 further, identify the key sources of elevated air pollution events, and design efficient emission control strategies to reduce O3 and PM2.5 pollution in YRD. In this study, we predicted the air quality during the EXPLORE-YRD campaign using the Weather Research and Forecasting/Community Multiscale Air Quality modelling system (WRF/CMAQ) and evaluated model performance on O3 and PM2.5 concentrations and compositions. Air quality was predicted using two sets of reanalysis data—NCEP Final (FNL) Operational Global Analysis and ECMWF Reanalysis v5.0 (ERA5)—and three horizontal resolutions of 36, 12, and 4 km. The results showed that PM2.5 concentration was generally under-predicted using both the FNL and ERA5 data. ERA5 yielded slightly higher PM2.5 predictions during the EXPLORE-YRD campaign. Both reanalysis data sets under-predicted the high PM2.5 pollution processes on 29–30 May 2018, indicating that reanalysis data is not essential for under-predicting extreme PM2.5 pollution processes. The performance of O3 was similar in both the reanalysis data sets, because O3 is mostly sensitive to temperature predictions and FNL and ERA5 yielded similar temperature results. Although the average performance of PM2.5 and O3 predictions yielded by FNL and ERA5 was similar, large differences were observed in certain locations on specific days (e.g. in Hangzhou between 29 May and June 6, 2018 and in Hefei on 1–3 June 2018). Therefore, the choice of reanalysis data could be an important factor affecting the predictions of PM2.5 and O3, depending on locations and episodes. Comparable results were obtained using predictions with different horizontal resolutions, indicating that grid resolution was not crucial for determining the model performance of both PM2.5 and O3 during the campaign.
•Air quality is predicted using WRF/CMAQ for the EXPLORE-YRD campaign.•ERA5 reanalysis data yield slightly better PM2.5 predictions than FNL, but both underpredict the high PM2.5 events.•O3 performance is similar with ERA5 and FNL.•Grid resolution is not a key factor for modelling PM2.5 and O3 in YRD.
Nitrated phenols (NPs) are important atmospheric pollutants that affect air quality, radiation, and health. The recent development of the time-of-flight chemical ionization mass spectrometer ...(ToF-CIMS) allows quantitative online measurements of NPs for a better understanding of their sources and environmental impacts. Herein, we deployed nitrate ions as reagent ions in the ToF-CIMS and quantified six classes of gaseous NPs in Beijing. The concentrations of NPs are in the range of 1 to 520 ng m–3. Nitrophenol (NPh) has the greatest mean concentration. Dinitrophenol (DNP) shows the greatest haze-to-clean concentration ratio, which may be associated with aqueous production. The high concentrations and distinct diurnal profiles of NPs indicate a strong secondary formation to overweigh losses, driven by high emissions of precursors, strong oxidative capacity, and high NO x levels. The budget analysis on the basis of our measurements and box-model calculations suggest a minor role of the photolysis of NPs (<1 ppb h–1) in producing OH radicals. NPs therefore cannot explain the underestimated OH production in urban environments. Discrepancies between these results and the laboratory measurements of the NP photolysis rates indicate the need for further studies aimed at understanding the production and losses of NPs in polluted urban environments.
The first OH and HO2 radical observation in Yangtze River Delta, one of the four major urban agglomerations in China, was carried out at a suburban site (Taizhou) in summer 2018 from May to June, ...aiming to elucidate the atmospheric oxidation capacity in this region. The maximum diurnal averaged OH and HO2 concentrations were 1.0×107 and1.1×109 cm-3, respectively, which were the second highest HOx (sum of OH and HO2) radical concentrations observed in China. HONO photolysis was the dominant radical primary source, accounting for 42 % of the total radical initiation rate. Other contributions were from carbonyl photolysis (including HCHO, 24 %), O3 photolysis (17 %), alkene ozonolysis (14 %), and NO3 oxidation (3 %). A chemical box model based on the RACM2-LIM1 mechanism could generally reproduce the observed HOx radicals, but systematic discrepancy remained in the afternoon for the OH radical, when the NO mixing ratio was less than 0.3 ppb. An additional recycling mechanism equivalent to 100 ppt NO was capable to fill the gap. The sum of monoterpenes was on average up to 0.4 ppb during daytime, which was all allocated to α-pinene in the base model. A sensitivity test without monoterpene input showed the modeled OH and HO2 concentrations would increase by 7 % and 4 %, respectively, but modeled RO2 concentration would significantly decrease by 23 %, indicating that monoterpene was an important precursor of RO2 radicals in this study. Consequently, the daily integrated net ozone production would reduce by 6.3 ppb without monoterpene input, proving the significant role of monoterpene in the photochemical O3 production in this study. In addition, the generally good agreement between observed and modeled HOx concentrations suggested no significant HO2 heterogeneous uptake process during this campaign. Incorporation of HO2 heterogeneous uptake process would worsen the agreement between HOx radical observation and simulation, and the discrepancy would be beyond the combined measurement–model uncertainties using an effective uptake coefficient of 0.2. Finally, the ozone production efficiency (OPE) was only 1.7 in this study, a few folds lower than other studies in (sub)urban environments. The low OPE indicated a slow radical propagation rate and short chain length. As a consequence, ozone formation was suppressed by the low NO concentration in this study.
HONO is an important precursor for OH radicals that impact secondary-pollutant production. However, there are still large uncertainties about different HONO sources which hinder accurate predictions ...of HONO concentration and hence atmospheric oxidation capacity. Here HONO was measured during the EXPLORE-YRD campaign (EXPeriment on the eLucidation of the atmospheric Oxidation capacity and aerosol foRmation and their Effects in the Yangtze River Delta), along with other important parameters, enabling us to comprehensively investigate HONO variation characteristics and evaluate the relative importance of different HONO sources by using a box model. HONO showed significant variations, ranging from several tens of parts per thousand to 4.4 ppb. The average diurnal pattern of HONO / NO.sub.x showed a maximum of 0.17 around noon and resembled that of j(O.sup.1 D), indicating the existence of photo-induced sources. Modeling simulations with only the default HONO source (OH + NO) largely underestimated HONO concentrations, with the modeled-averaged noontime HONO concentration an order of magnitude lower than the observed concentration. The calculated strength of the unknown HONO source (P.sub.unknown) showed a nearly symmetrical diurnal profile with a maximum of 2.5 ppb h.sup.-1 around noon. The correlation analysis and sensitivity tests showed that the photo-induced NO.sub.2 conversion on the ground was able to explain P.sub.unknown . Additional HONO sources incorporated into the box model improved the model's performance in simulating HONO concentrations. The revised box model reproduced the nighttime HONO concentration well but still underestimated the daytime HONO concentration. Further sensitivity tests indicated the underestimation of daytime HONO was not due to uncertainties of photo-induced NO.sub.2 uptake coefficients on the ground or aerosol surfaces or the enhancement factor of nitrate photolysis but was more likely due to other sources that were not considered in the model. Among the incorporated HONO sources and the default gas-phase source, photo-induced NO.sub.2 conversion on the ground dominated the modeled HONO production during the daytime, accounting for 71 % of the total, followed by NO + OH, NO.sub.2 hydrolysis on the ground surface, vehicle emissions, photo-induced NO.sub.2 conversion on the aerosol surface, nitrate photolysis and NO.sub.2 hydrolysis on the aerosol surface. NO.sub.2 hydrolysis on the ground surface was the major source of nighttime HONO, contributing 55 % of total HONO production. HONO photolysis contributed 43 % of RO.sub.x production during the daytime, followed by O.sub.3 photolysis (17 %), HCHO photolysis (14 %), ozonolysis of alkenes (12 %) and carbonyl photolysis (10 %). With observed HONO as a model constraint, the average peak of net ozone production rate increased by 88 % to 12.6 ppb h.sup.-1 compared to that without observed HONO as a model constraint, indicating HONO evidently enhanced O.sub.3 production and hence aggravated O.sub.3 pollution in summer seasons. Our study emphasized the importance of heterogeneous NO.sub.2 conversion on the ground surface in HONO production and accurate parameterization of HONO sources in predicting secondary-pollutant production.
HONO is an important precursor for OH radicals that impact secondary-pollutant production. However, there are still large uncertainties about different HONO sources which hinder accurate predictions ...of HONO concentration and hence atmospheric oxidation capacity. Here HONO was measured during the EXPLORE-YRD campaign (EXPeriment on the eLucidation of the atmospheric Oxidation capacity and aerosol foRmation and their Effects in the Yangtze River Delta), along with other important parameters, enabling us to comprehensively investigate HONO variation characteristics and evaluate the relative importance of different HONO sources by using a box model. HONO showed significant variations, ranging from several tens of parts per thousand to 4.4 ppb. The average diurnal pattern of HONO / NOx showed a maximum of 0.17 around noon and resembled that of j(O1D), indicating the existence of photo-induced sources. Modeling simulations with only the default HONO source (OH + NO) largely underestimated HONO concentrations, with the modeled-averaged noontime HONO concentration an order of magnitude lower than the observed concentration. The calculated strength of the unknown HONO source (Punknown) showed a nearly symmetrical diurnal profile with a maximum of 2.5 ppb h−1 around noon. The correlation analysis and sensitivity tests showed that the photo-induced NO2 conversion on the ground was able to explain Punknown. Additional HONO sources incorporated into the box model improved the model's performance in simulating HONO concentrations. The revised box model reproduced the nighttime HONO concentration well but still underestimated the daytime HONO concentration. Further sensitivity tests indicated the underestimation of daytime HONO was not due to uncertainties of photo-induced NO2 uptake coefficients on the ground or aerosol surfaces or the enhancement factor of nitrate photolysis but was more likely due to other sources that were not considered in the model. Among the incorporated HONO sources and the default gas-phase source, photo-induced NO2 conversion on the ground dominated the modeled HONO production during the daytime, accounting for 71 % of the total, followed by NO + OH, NO2 hydrolysis on the ground surface, vehicle emissions, photo-induced NO2 conversion on the aerosol surface, nitrate photolysis and NO2 hydrolysis on the aerosol surface. NO2 hydrolysis on the ground surface was the major source of nighttime HONO, contributing 55 % of total HONO production. HONO photolysis contributed 43 % of ROx production during the daytime, followed by O3 photolysis (17 %), HCHO photolysis (14 %), ozonolysis of alkenes (12 %) and carbonyl photolysis (10 %). With observed HONO as a model constraint, the average peak of net ozone production rate increased by 88 % to 12.6 ppb h−1 compared to that without observed HONO as a model constraint, indicating HONO evidently enhanced O3 production and hence aggravated O3 pollution in summer seasons. Our study emphasized the importance of heterogeneous NO2 conversion on the ground surface in HONO production and accurate parameterization of HONO sources in predicting secondary-pollutant production.
Due to the development of industrialization and urbanization, secondary pollution is becoming increasingly serious in the Yangtze River Delta. Volatile organic compounds (VOCs) are key precursors of ...the near-surface ozone, secondary organic aerosol (SOA), and other secondary pollutants. In this study, we chose a serious ozone pollution period (01 May–31 July 2017) in Jinshan, which is a petrochemical and industrial area in Shanghai. We explored the VOCs distribution characteristics and contribution to secondary pollutants via constructing a regional network based on wind patterns. We determined that dense pollutants were accumulated at adjacent sites under local circulation (LC), and pollution from petrochemical discharge was more serious than industry for all sites under southeast (SE) wind. We also found that cyclopentane, o-xylene, m/p-xylene, 1-3-butadiene, and 1-hexene were priority-controlled species as they were most vital to form secondary pollutants. This study proves that regional network analysis can be successfully applied to explore pollution characteristics and regional secondary pollutants formation.
HONO is an important precursor for OH radicals that impact secondary-pollutant production. However, there are still large uncertainties about different HONO sources which hinder accurate predictions ...of HONO concentration and hence atmospheric oxidation capacity. Here HONO was measured during the EXPLORE-YRD campaign (EXPeriment on the eLucidation of the atmospheric Oxidation capacity and aerosol foRmation and their Effects in the Yangtze River Delta), along with other important parameters, enabling us to comprehensively investigate HONO variation characteristics and evaluate the relative importance of different HONO sources by using a box model. HONO showed significant variations, ranging from several tens of parts per thousand to 4.4 ppb. The average diurnal pattern of HONO / NOx showed a maximum of 0.17 around noon and resembled that of j(O1D), indicating the existence of photo-induced sources. Modeling simulations with only the default HONO source (OH + NO) largely underestimated HONO concentrations, with the modeled-averaged noontime HONO concentration an order of magnitude lower than the observed concentration. The calculated strength of the unknown HONO source (Punknown) showed a nearly symmetrical diurnal profile with a maximum of 2.5 ppb h-1 around noon. The correlation analysis and sensitivity tests showed that the photo-induced NO2 conversion on the ground was able to explain Punknown. Additional HONO sources incorporated into the box model improved the model's performance in simulating HONO concentrations. The revised box model reproduced the nighttime HONO concentration well but still underestimated the daytime HONO concentration. Further sensitivity tests indicated the underestimation of daytime HONO was not due to uncertainties of photo-induced NO2 uptake coefficients on the ground or aerosol surfaces or the enhancement factor of nitrate photolysis but was more likely due to other sources that were not considered in the model. Among the incorporated HONO sources and the default gas-phase source, photo-induced NO2 conversion on the ground dominated the modeled HONO production during the daytime, accounting for 71 % of the total, followed by NO + OH, NO2 hydrolysis on the ground surface, vehicle emissions, photo-induced NO2 conversion on the aerosol surface, nitrate photolysis and NO2 hydrolysis on the aerosol surface. NO2 hydrolysis on the ground surface was the major source of nighttime HONO, contributing 55 % of total HONO production. HONO photolysis contributed 43 % of ROx production during the daytime, followed by O3 photolysis (17 %), HCHO photolysis (14 %), ozonolysis of alkenes (12 %) and carbonyl photolysis (10 %). With observed HONO as a model constraint, the average peak of net ozone production rate increased by 88 % to 12.6 ppb h-1 compared to that without observed HONO as a model constraint, indicating HONO evidently enhanced O3 production and hence aggravated O3 pollution in summer seasons. Our study emphasized the importance of heterogeneous NO2 conversion on the ground surface in HONO production and accurate parameterization of HONO sources in predicting secondary-pollutant production.
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