Although the concentrations of five basic ambient air pollutants in the Yangtze River Delta (YRD) have been reduced since the implementation of the \"Air Pollution Prevention and Control Action Plan\" in 2013, the ozone concentrations still increase. In order to explore the causes of ozone pollution in YRD, we use the GEOS-Chem and its adjoint model to study the sensitivities of ozone to its precursor emissions from different source regions and emission sectors during heavy ozone pollution events under typical circulation patterns. The Multi-resolution Emission Inventory for China (MEIC) of Tsinghua University and 0.25° × 0.3125° nested grids are adopted in the model. By using the T-mode principal component analysis (T-PCA), the circulation patterns of heavy ozone pollution days (observed MDA8 O3 concentrations ≥160 μg m-3) in Nanjing located in the center area of YRD from 2013 to 2019 are divided into four types, with the main features of Siberian Low, Lake Balkhash High, Northeast China Low, Yellow Sea High, and southeast wind at the surface. The adjoint results show that the contributions of emissions emitted from Jiangsu and Zhejiang are the largest to heavy ozone pollution in Nanjing. The 10 % reduction of anthropogenic NOx and NMVOCs emissions in Jiangsu, Zhejiang and Shanghai could reduce the ozone concentrations in Nanjing by up to 3.40 μg m-3 and 0.96 μg m-3, respectively. However, the reduction of local NMVOCs emissions has little effect on ozone concentrations in Nanjing, and the reduction of local NOx emissions would even increase ozone pollution. For different emissions sectors, industry emissions account for 31 %-74 % of ozone pollution in Nanjing, followed by transportation emissions (18 %-49 %). This study could provide the scientific basis for forecasting ozone pollution events and formulating accurate strategies of emission reduction.

Understanding the causes and sources responsible for severe fine particulate matter (PM2.5) pollution episodes that occur under conducive synoptic weather patterns (SWPs) is essential for regional air quality management. The Yangtze River Delta (YRD) region in eastern China has experienced recurrent severe PM2.5 episodes during the winters from 2013 to 2017. In this study, we employed an objective classification approach, the self-organizing map, to investigate the underlying impact of predominant SWPs on PM2.5 pollution in the YRD. We further conducted a series of source apportionment simulations using the Particulate Source Apportionment Technology (PSAT) tool integrated within the Comprehensive Air Quality Model with Extensions (CAMx) to quantify the source contributions to PM2.5 pollution under different SWPs. Here we identified six predominant SWPs over the YRD that are robustly connected to the evolution of the Siberian High. Considering the regional average PM2.5 anomalies, our results show that polluted SWPs favourable for the occurrence of regional PM2.5 pollution account for 61-78 %. The most conducive SWP, associated with the highest regional exceedance (46 %) of PM2.5 levels, is characterized by noticeable cyclonic anomalies at 850 hPa and stagnant surface weather conditions. Our source apportionment analysis emphasizes the pivotal role of local emissions and intra-regional transport within the YRD in shaping PM2.5 pollution in representative cities. Local emissions have the most significant impact on PM2.5 levels in Shanghai (32-48 %), while PM2.5 pollution in Nanjing, Hangzhou, and Hefei is more influenced by intra-regional transport (33-61 %). Industrial and residential emissions are the dominant sources, contributing 32-41 % and 24-38 % to PM2.5, respectively. Under specific SWPs associated with a stronger influence of inter-regional transport from northern China, there is a synchronously remarkable enhancement in the contribution of residential emissions. Our study pinpoints the opportunities for future air quality planning that would benefit from quantitative source attribution linked to prevailing SWPs.

Based on the ISAM module in the WRF-CMAQ model, this study analyzed the source contribution(both regional and sectoral) of O3 and its precursors(NO2 and VOCs) in Zibo in June 2021. Days with a maximum daily 8-h average(MDA8) O3 higher(lower) than 160 μg·m-3 were defined as polluted(clean) days. Differences in the source contribution between clean days and polluted days were compared, and a typical pollution period was selected for further process analysis. The results showed that NO2 in Zibo mainly came from local emissions in summer, with a relative contribution of 45.1%. Vehicle emissions(33.8%) and natural sources(20.7%) were the primary NO2 sources. VOC contributions from natural sources, solvent usage, and the petrochemical industry were significant, with a total contribution of 78.5%. The MDA8 contribution from local sources was 21.4%, whereas the impact of regional transport(32%) and surrounding cities(26.8%) was also substantial. Among local emission sources, vehicle emissions, the power industry, and the building materials industry contributed 10.9%-18.8% to local MDA8. On O3 pollution days, the MDA8 contribution from local emissions and surrounding cities increased. However, the relative contributions from local sources were similar under different pollution conditions.

Snowpack, which serves as a natural archive of atmospheric deposition of multiple pollutants, is a practical environmental media that can be used for assessing atmospheric records and input of the pollutants to the surface environments and ecosystems. A total of 29 snowpack samples were collected at 20 sampling sites covering three different functional areas of a major city (Harbin) in Northeast China. Two samples at the \"snow layer\" and one or two samples at the \"particulate layer\" were collected at each sampling site in the industrial areas characterized by multi-layer snowpack, and only one sample at the \"snow layer\" was collected at each sampling site in the cultural and recreational as well as agricultural areas. The snow contents of 31 elements (Na, Mg, Al, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Y, Cd, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, and Pb) and six major water-soluble inorganic ions (WSIIs, NH4+, K+, Ca2+, NO2-, NO3-, and SO42-) were analyzed. The total mass of the measured elements is dominated (95.8%-99.2%) by crustal elements. Heavy metals only account for 0.77%-4.07% of the total mass of the elements, but are occasionally close to or even above the standard limit in the \"Environmental Quality Standards for Surface Water\" of China (GB3838-2002). SO42- and Ca2+ are the main anion and cation, accounting for 34.9%-81.1% and 1.43%-29.9%, respectively, of the measured total ions. Total atmospheric deposition of crustal elements and heavy metals is dominated by wet deposition in areas near the petrochemical plant and by dry deposition in areas near the cement plant. Coal combustion, industrial emissions, and traffic-related activities lead to the enrichment of heavy metals in the snowpacks of urban and suburban areas, while coal combustion and biomass burning contribute to pollution in rural areas. The cities and regions situated in the western, northwestern, northern, and northeastern directions from Harbin are potential source regions of these pollutant species.

Nowadays, a large number of compounds with different physical and chemical properties have been determined in soil. Environmental behaviors and source identification of pollutants in soil are the foundation of soil pollution control. Identification and quantitative analysis of potential pollution sources are the prerequisites for its prevention and control. Many efforts have made to develop methods for identifying the sources of soil pollutants. These efforts have involved the measurement of source and receptor parameters and the analysis of their relationships via numerical statistics methods. We have comprehensively reviewed the progress made in the development of source apportionment methodologies to date and present our synthesis. The numerical methods, such as spatial geostatistics analysis, receptor models, and machine learning methods are addressed in depth. In most cases, however, the effectiveness of any single approach for source apportionment remains limited. Combining multiple methods to address soil quality problems can reduce uncertainty about the sources of soil pollution. This review also constructively highlights the key strategies of combining mathematical models with the assessment of chemical profiles to provide more accurate source attribution. This review intends to provide a comprehensive summary of source apportionment methodologies to help promote further development.

The Yangtze River Delta (YRD) region frequently experiences ozone pollution events during the summer and autumn seasons. High-concentration events are typically related to synoptic weather patterns, which impact the transport and photochemical production of ozone at multiple scales, ranging from the local to regional scale. To better understand the regional ozone pollution problem, studies on ozone source attribution are needed, especially regarding the contributions of sources at different vertical heights based on tagging the region or time periods. Between September 3 and 8, 2020, an episode of ozone concentration anomaly high was observed in Hefei through ground-based stations and ozone Lidar. The mechanism behind this event was uncovered through synoptic weather pattern analysis and using the Weather Research and Forecasting Chemistry model (WRF-Chem). Because an approaching typhoon caused variable wind direction, the O3-rich air masses (ORMs) arising from the YRD region were transported to Hefei via the nocturnal residual layer and descended to the ground through horizontal advection and vertical mixing processes the next day. Based on geographic source tagging, the anthropogenic NOx emissions (ANEs) from local and regional sources were the main contributors to the heavy ozone pollution over Hefei on September 6. Furthermore, the intra-regional transported ozone from southern Jiangsu (SJS), southern Anhui (SAH), and Zhejiang (ZJ) in the YRD was the main driving factor of the surface and upper atmosphere ozone pollution. Based on time period tagging, The ozone generated due to ANEs from September 3 to 5 significantly contributed to this episode. It is important to pay attention to the impact of ANEs on September 5 on the surface peak ozone concentration the following day (i.e., September 6). Our findings provide significant insights into the regional ozone transport mechanism in the YRD and optimization of measures to prevent and control heavy ozone pollution on spatiotemporal scales.

Volatile methyl siloxanes (VMS) are ubiquitous in indoor environments due to their use in personal care products. This paper builds on previous work identifying sources of VMS by synthesizing time-resolved proton-transfer reaction time-of-flight mass spectrometer VMS concentration measurements from four multiweek indoor air campaigns to elucidate emission sources and removal processes. Temporal patterns of VMS emissions display both continuous and episodic behavior, with the relative importance varying among species. We find that the cyclic siloxane D5 is consistently the most abundant VMS species, mainly attributable to personal care product use. Two other cyclic siloxanes, D3 and D4, are emitted from oven and personal care product use, with continuous sources also apparent. Two linear siloxanes, L4 and L5, are also emitted from personal care product use, with apparent additional continuous sources. We report measurements for three other organosilicon compounds found in personal care products. The primary air removal pathway of the species examined in this paper is ventilation to the outdoors, which has implications for atmospheric chemistry. The net removal rate is slower for linear siloxanes, which persist for days indoors after episodic release events. This work highlights the diversity in sources of organosilicon species and their persistence indoors.

Large airport operation release harmful air pollutants and have adverse impact on local air quality. As one of the world\'s top 30 busiest airports, Xinzheng International Airport (CGO) located in Zhengzhou City, China, its emission impacts needs particular attention. To identify the possible impacts and quantify the contribution of CGO airport to air pollution, a comprehensive approach including emission inventory, continuous monitoring, coupled with statistical modelling was adopted in this study. We estimated a more detailed emissions inventory for CGO, including hourly and annual emissions from engines and auxiliary power units of aircrafts during landing and take-off flight, and airside ground support equipment (GSE) in 2019. The results indicate that almost all the CGO specific parameters including operating hours, fuel consumption and unit LTO emissions at different modes were lower than ICAO reference values. The annual total emissions of NOx, CO, HC, SO2 and PM from CGO from aircrafts and GSE were 1207.7, 921.3, 123.7, 268.3 and 36.2 tons, respectively. In addition to SO2, the main engines of the aircraft accounted for 80.3%, 62.6%, 45.5% and 74.3% of the total emissions, respectively. Meanwhile, a continuous monitoring campaign was conducted for one year in the vicinity of CGO airport. The monitoring data were analyzed using generalized additive model (GAM) to quantify the impact of NOx emissions from airport activities on air quality at CGO. The results showed that even the influence of environmental and meteorological variables was greater, nearly 13% of the ambient NOx concentrations were explained by emissions from airport activities, indicating the importance of airport-related emissions as the major source affecting local air quality.

Nitrogen (N) management is essential for food security. The North China Plain is an important food producing region, but also a hotspot of N losses to the environment. This results in water, soil, and air pollution. In this study, we aim to quantify the relative contribution of different crops and animals to N losses, by taking the Quzhou county as a typical example in the North China Plain. We developed and applied a new version of the NUtrient flows in Food chains, Environment, and Resource use (NUFER) model. Our model is based on updated information for N losses in Quzhou. Our results show that N losses to the environment from crop and animal production in Quzhou were approximately 9 kton in 2017. These high N losses can be explained by the low N use efficiency in food production because of poor N management. For crop production, wheat, maize, and vegetables contributed 80% to N losses. Ammonia emissions and N leaching have dominant shares in these N losses. Pigs and laying hens were responsible for 74% of N losses from animal production. Ammonia emissions to air and direct discharges of manure to water were the main contributors to these N losses. Effective reduction of N losses requires improving the nutrient management in crop (wheat, maize, vegetables) and animal (pigs, laying hens) production. Our work could support the Agricultural Green Development in the North China Plain.

Pien-Tze-Huang is one of the most famous traditional Chinese medicine prescriptions and consists of several precious medicinal materials, such as Notoginseng Radix et Rhizoma, Bovis Calculus, Snake Gall, and Moschus. However, its formula has not been completely revealed. It is mainly applied for the treatment of acute and chronic viral hepatitis, carbuncle, and boils caused by blood stasis, unknown swelling, bruises, and various inflammation disorders. The chemical composition of Pien-Tze-Huang is extremely complicated. Thus far, extensive attention has been paid to the principal chemical families in Pien-Tze-Huang, such as ginsenosides, bile acids, and muscone derivatives. Comprehensive chemical profiling, although of immense importance for systematic quality control, has not been achieved. Therefore, we configured a platform, namely online pressurized liquid extraction-ultra-high-performance liquid chromatography-ion trap-time-of-flight mass spectrometry (online PLE-UHPLC-IT-TOF-MS), to characterize the chemical profile of Pien-Tze-Huang in detail as well as to conduct source attribution, aiming to clarify the chemome of Pien-Tze-Huang and to provide a reliable method for quality assessment. A sub-microgram amount of Pien-Tze-Huang powder (0.3 mg) was placed in a hollow guard column, which was subsequently filled with clear silica gel. Filter membranes were used to seal the extraction vessel. The vessel was then placed in an adapted guard column holder and maintained in a thermal column oven (70 ℃). Metal tubing was used to connect the outlet of the guard column holder to the mass spectrometer. The extraction phase was maintained for 3 min by employing 0.1%(v/v) formic acid aqueous solution as the extraction solvent with a flow rate of 0.2 mL/min. Moreover, a six-port two-position electronic valve was introduced to automatically switch the system from extraction to elution phases. Within the elution phase, 0.1%(v/v) formic acid aqueous solution and acetonitrile composed the mobile phase, and the extracts were eluted with a gradient program. Because of the elevated temperature and pressure, the physical and chemical properties of water, especially polarity and solubility, were modified. Therefore, warm water could be an eligible green solvent to achieve wide polarity-spanned extraction. In addition, IT-TOF-MS was employed to acquire tandem mass spectrometry information. The mass fragmentation pathways of saponins and bile acids were carefully studied. Finally, according to authentic compounds, mass fragmentation pathways, reference information in the literature, and accessible databanks, a total of 73 signals were observed from Pien-Tze-Huang, of which 71 components were tentatively identified and assigned. Among them, 36 were from Notoginseng Radix et Rhizoma, 15 from Snake Gall, and 9 from Bovis Calculus, while the occurrences of the other 11 components were synergistically contributed by both Bovis Calculus and Snake Gall, through retrieving the in-house chemical database that was built by considering all accessible chemical information from Notoginseng Radix et Rhizoma, Bovis Calculus, Snake Gall, and Moschus. The other two compounds were assigned as unknown compounds. However, none of the components were assigned to Moschus because they mainly contained hydrophobic compounds, such as cycloketones, cholesterol, and sterols, among others, and it was difficult to detect them with the current measurement program. The extraction efficiency of online PLE was assessed by comparing it with the efficiency obtained from ultrasonication at the same time. According to base peak ion current chromatograms (BPCs) and mass spectrometry information, the efficiency of online PLE was greater than that of ultrasonic extraction, even through direct analysis. Online PLE-UHPLC-IT-TOF-MS is not only a tool fit for the concept of green analytical chemistry, but also a reliable analytical pipeline for the direct characterisation of other complicated matrixes. Above all, this study clarified the chemome of Pien-Tze-Huang and provided meaningful information for the quality control of this famous TCM prescription.