Gainesclemmensen2044

The changes of emission ratios of VOCs between winter 2011 and winter 2018 in the NCP support the positive effect of "coal to gas" strategies in curbing air pollutants. The high abundances of some key species (e.g. oxygenated aromatics) indicate the strong emissions of coal combustion in wintertime of NCP. The ratio of naphthalene to C8 aromatics was proposed as a potential indicator of the influence of coal combustion on VOCs.The field observation of 54 non-methane hydrocarbon compounds (NMHCs) was conducted from September 1 to October 20 in 2020 during autumn in Haidian District, Beijing. The mean concentration of total NMHCs was 29.81 ± 11.39 ppbv during this period, and alkanes were the major components. There were typical festival effects of NMHCs with lower concentration during the National Day. Alkenes and aromatics were the dominant groups in ozone formation potential (OFP) and OH radical loss rate (LOH). The positive matrix factorization (PMF) running results revealed that vehicular exhaust became the biggest source in urban areas, followed by liquefied petroleum gas (LPG) usage, solvent usage, and fuel evaporation. The box model coupled with master chemical mechanism (MCM) was applied to study the impacts of different NMHCs sources on ozone (O3) formation in an O3 episode. The simulation results indicated that reducing NMHCs concentration could effectively suppress O3 formation. Moreover, reducing traffic-related emissions of NMHCs was an effective way to control O3 pollution at an urban site in Beijing.Fine particulate matter (PM2.5) and ozone (O3) pollutions are prevalent air quality issues in China. Volatile organic compounds (VOCs) have significant impact on the formation of O3 and secondary organic aerosols (SOA) contributing PM2.5. Herein, we investigated 54 VOCs, O3 and SOA in Tianjin from June 2017 to May 2019 to explore the non-linear relationship among O3, SOA and VOCs. The monthly patterns of VOCs and SOA concentrations were characterized by peak values during October to March and reached a minimum from April to September, but the observed O3 was exactly the opposite. Machine learning methods resolved the importance of individual VOCs on O3 and SOA that alkenes (mainly ethylene, propylene, and isoprene) have the highest importance to O3 formation; alkanes (Cn, n ≥ 6) and aromatics were the main source of SOA formation. Machine learning methods revealed and emphasized the importance of photochemical consumptions of VOCs to O3 and SOA formation. Ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) calculated by consumed VOCs quantitatively indicated that more than 80% of the consumed VOCs were alkenes which dominated the O3 formation, and the importance of consumed aromatics and alkenes to SOAFP were 40.84% and 56.65%, respectively. Therein, isoprene contributed the most to OFP at 41.45% regardless of the season, while aromatics (58.27%) contributed the most to SOAFP in winter. Collectively, our findings can provide scientific evidence on policymaking for VOCs controls on seasonal scales to achieve effective reduction in both SOA and O3.Ammonia (NH3) is ubiquitous in the atmosphere, it can affect the formation of secondary aerosols and particulate matter, and cause soil eutrophication through sedimentation. Currently, the use of radioactive primary reagent ion source and the humidity interference on the sensitivity and stability are the two major issues faced by chemical ionization mass spectrometer (CIMS) in the analysis of atmospheric ammonia. In this work, a vacuum ultraviolet (VUV) Kr lamp was used to replace the radioactive source, and acetone was ionized under atmospheric pressure to obtain protonated acetone reagent ions to ionize ammonia. The ionization source is designed as a separated three-zone structure, and even 90 vol.% high-humidity samples can still be directly analyzed with a sensitivity of sub-ppbv. A signal normalization processing method was designed, and with this new method, the quantitative relative standard deviation (RSD) of the instrument was decreased from 17.5% to 9.1%, and the coefficient of determination was increased from 0.8340 to 0.9856. The humidity correction parameters of the instrument were calculated from different humidity, and the ammonia concentrations obtained under different humidity were converted to its concentration under zero humidity condition with these correction parameters. The analytical time for a single sample is only 60 sec, and the limit of detection (LOD) was 8.59 pptv (signal-to-noise ratio S/N = 3). The ambient measurement made in Qingdao, China, in January 2021 with this newly designed CIMS, showed that the concentration of ammonia ranged from 1 to 130 ppbv.The Asian Tropopause Aerosols Layer (ATAL) refers to an accumulation of aerosols in the upper troposphere and lower stratosphere during boreal summer over Asia, which has a fundamental impact on the monsoon system and climate change. In this study, we primarily analyze the seasonal to sub-seasonal variations of the ATAL and the factors potentially influencing those variations based on MERRA2 reanalysis. The ability of the reanalysis to reproduce the ATAL is well validated by CALIPSO observations from May to October 2016. The results reveal that the ATAL has a synchronous spatiotemporal pattern with the development and movement of the Asian Summer Monsoon. Significant enhancement of ATAL intensity is found during the prevailing monsoon period of July-August, with two maxima centered over South Asia and the Arabian Peninsula. Owing to the fluctuations of deep convection, the ATAL shows an episodic variation on a timescale of 7-12 days. Attribution analysis indicates that deep convection dominates the variability of the ATAL with a contribution of 62.7%, followed by a contribution of 36.6% from surface pollutants. The impact of precipitation is limited. The ATAL further shows a clear diurnal variation the peak of ATAL intensity occurs from 1730 to 2330 local time (LT), when the deep convection becomes strongest; the minimum ATAL intensity occurs around 830 LT owing to the weakened deep convection and photochemical reactions in clouds. The aerosol components of the ATAL show different spatiotemporal patterns and imply that black carbon and organic carbon come mainly from India, whereas sulfate comes mainly from China during the prevailing monsoon period.Spatiotemporal variations of ozone (O3) taken from the Copernicus Atmosphere Monitoring Service (CAMS) and the second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) were intercompared and evaluated with ground and ozone-sonde observations over China in 2018 and 2019. Intercomparison of the surface ozone from CAMS and MERRA-2 reanalysis showed significant negative bias (CAMS minus MERRA-2, same below) at Tibetan Plateau of up to 80 µg/m3, and the average R2 was about 0.6 across China. Evaluated with the ground observations from China National Environmental Monitoring Center (CNEMC), we found that CAMS and MERRA-2 reanalysis were capable of capturing the key patterns of monthly and diurnal variations of surface ozone over China except for the western region, and MERRA-2 overestimated the observations compared to CAMS. Vertically, the CAMS profiles overestimated the ozone-sonde from the World Ozone and Ultraviolet Radiation Data Center (WOUDC) above 200 hPa with the magnitude reaching up to 150 µg/m3, while little bias was found between the reanalysis and observations below 200 hPa. Intercomparison drawn from the vertical distribution between CAMS and MERRA-2 reanalysis showed that the negative bias appeared throughout the troposphere over China, while the positive bias emerged in the upper troposphere and lower stratosphere (UTLS) with high order of magnitude exceeding 100 µg/m3, indicating large uncertainties at higher altitudes. In summary, we concluded that CAMS reanalysis showed better agreement with the observations in contrast to MERRA-2, and the large discrepancy especially at higher altitudes between these two reanalysis datasets could not be ignored.Air pollution in China is complex, and the formation mechanism of chemical components in particulate matter is still unclear. This study selected three consecutive heavy haze pollution episodes (HPEs) during winter in Beijing for continuous field observation, including an episode with heavy air pollution under red alert. Clean days during the observation period were selected for comparison. The HPE characteristics of Beijing in winter were under the influence of adverse meteorological conditions such as high relative humidity, temperature inversion and low wind speed; and strengthening of secondary transformation reactions, which further intensified the accumulation of secondary aerosols and other pollutants, promoting the explosive growth of PM2.5. PM2.5/CO values, as indicators of the contribution of secondary transformation in PM2.5, were approximately 2 times higher in the HPEs than the average PM2.5/CO during the clean period. The secondary inorganic aerosols (sulfate nitrate and ammonium salt) were significantly enhanced during the HPEs, and the conversion coefficients were remarkably improved. In addition, it is interesting to observe that the production of sulfate tended to exceed that of nitrate in the late stage of all three HPEs. The existence of aqueous phase reactions led to the explosive growth sulfur oxidation ratio (SOR) and rapid generation of sulfate under high relative humidity (RH>70%).The intraurban distribution of PM2.5 concentration is influenced by various spatial, socioeconomic, and meteorological parameters. This study investigated the influence of 37 parameters on monthly average PM2.5 concentration at the subdistrict level with Pearson correlation analysis and land-use regression (LUR) using data from a subdistrict-level air pollution monitoring network in Shenzhen, China. Performance of LUR models is evaluated with leave-one-out-cross-validation (LOOCV) and holdout cross-validation (holdout CV). Pearson correlation analysis revealed that Normalized Difference Built-up Index, artificial land fraction, land surface temperature, and point-of-interest (POI) numbers of factories and industrial parks are significantly positively correlated with monthly average PM2.5 concentrations, while Normalized Difference Vegetation Index and Green View Factor show significant negative correlations. For the sparse national stations, robust LUR modelling may rely on a priori assumptions in direction of influence during the predictor selection process. The month-by-month spatial regression shows that RF models for both national stations and all stations show significantly inflated mean values of R2 compared with cross-validation results. For MLR models, inflation of both R2 and R2CV was detected when using only national stations and may indicate the restricted ability to predict spatial distribution of PM2.5 levels. Inflated within-sample R2 also exist in the spatiotemporal LUR models developed with only national stations, although not as significant as spatial LUR models. Our results suggest that a denser subdistrict level air pollutant monitoring network may improve the accuracy and robustness in intraurban spatial/spatiotemporal prediction of PM2.5 concentrations.