A novel sample preparation method based on polarity grouping for comprehensive determination of 315 pesticides and other contaminants in wolfberry by liquid chromatography-tandem mass spectrometry.

A novel sample preparation method based on polarity grouping was developed for the comprehensive determination of 315 undesirable low-weight organic pollutants ranging from polar to weakly polar in wolfberry. The method involves the swelling of the sample in ammonium acetate buffer, two-phase extraction, three-phase extraction, and dispersive solid phase extraction (D-SPE) with the assistance of low-temperature centrifugation and analysis by ultrahigh performance liquid chromatography coupled with electrospray ionization tandem mass (UHPLC-ESI-MS-MS) by using the multiple reaction monitoring mode. The recoveries of the analytes with wide range of polarity were satisfactory. The matrix-fortified standard calibration curves were compared for quantification. The results of linearity were satisfactory with linear regression coefficients (R) ranging from 0.9901 to 1.000. The limits of quantification ranged from 1 µg/kg to 10.0 µg/kg, indicating the compliance of products with legal tolerances. The average recoveries for spiked wolfberry were in the range of 69.3 %-145.2 % with RSD values of 0.2 %-28.6 %. The inter-day precision was in the range of 0.2 %-27.0 %. For over 90 % of the analytes, the recoveries were 70 %-120 % with RSD values below 20 %. The application of this method in routine monitoring programs would imply a drastic reduction of both effort and time.

[Pollution Characteristics and Source Apportionment of Atmospheric Volatile Organic Compounds in Winter in Kaifeng City].

In order to explore the pollution characteristics and sources of atmospheric volatile organic compounds (VOCs) in winter in Kaifeng City, based on the atmospheric VOCs component data obtained from the online monitoring station of the Kaifeng Ecological and Environmental Bureau (Urban Area) from December 2021 to January 2022, the pollution characteristics of VOCs and secondary organic aerosol formation potential (SOAP) were discussed, and the sources of VOCs were analyzed by using the PMF model. The results showed that the average mass concentration of VOCs in winter in Kaifeng City was (104.71±48.56) µg·m-3, and alkanes (37.7%) had the highest proportion of mass concentrations, followed by that of halohydrocarbons (23.5%), aromatics (16.8%), OVOCs (12.6%), alkenes (6.9%), and alkynes (2.6%). The averaged total SOAP contributed by VOCs was 3.18 µg·m-3, of which aromatics contributed as much as 83.8%, followed by alkanes (11.5%). The largest anthropogenic source of VOCs in winter in Kaifeng City was solvent utilization (17.9%), followed by fuel combustion (15.9%), industrial halohydrocarbon emission (15.8%), motor vehicle emission (14.7%), organic chemical industry (14.5%), and LPG emission (13.3%); solvent utilization contributed 32.2% of the total SOAP, followed by motor vehicle emission (22.8%) and industrial halohydrocarbon emission (18.9%). It was found that reducing VOCs emissions from solvent utilization, motor vehicle emission, and industrial halohydrocarbon emission was important to control the formation of secondary organic aerosols in winter in Kaifeng City.

A generic and rapid analytical method for comprehensive determination of veterinary drugs and other contaminants in raw honey.

A generic, rapid, simple and low-cost analytical method for simultaneously screening and confirming 400 veterinary drugs and other contaminants in raw honey was developed. The method was based on one-step extraction and ultra-high performance liquid chromatography coupled with electrospray ionization and tandem mass spectrometry. A well-designed extraction method results in the complete precipitation of proteins and separation of analytes from carbohydrates and reduces the time and cost of analysis by covering polar to non-polar analytes. Optimized pretreatment processes lead to no significant interference on analysis from complicated sample matrix. Competent linearity was found for all of target compounds with linear regression coefficients (R) higher than 0.99. Detection limits ranged from 0.05 µg/kg to 10 µg/kg. Average recovery rates spiked in raw honey and ranged from 57.3% to 139.8%, and associated relative standard deviation values ranged from 0.4% to 25.2% under selected conditions. The extraction sensitivity, linearity, recovery, and precision of the method were validated. The results clearly demonstrated the feasibility of using this approach in raw honey purchase. The application of this method, which improved efficiency and the coverage of residues, may considerably reduce effort and time in routine monitoring programs.

[Accurate Identification of Ambient VOCs Emission Sources in an Industrial Park Using On-Line Monitoring Data].

To accurately identify and locate ambient volatile organic compounds(VOCs)emission sources in industrial parks, a continuous online GC-FID method was used to monitor 43 kinds of VOCs on an hourly basis during January 2017 at five sites in an industrial park. A statistical analysis and a PMF model were used to analyze the sources of VOCs, and by combining with CPF and enterprise emission information, the location of each pollution source was accurately identified. The average VOCs concentration was 56.40×10-9 and the highest concentration of alkanes was observed at four sites, with the exception of one site. Ethane, propane, ethylene, toluene, isobutane, n-butane, and acetylene were the main contributors. Ambient VOCs in the park mainly derives from five sources:urban transmission, butane leakage, process emissions, storage tank emissions, and ethylene synthesis. The enterprises in the zone B1, A1-A3, C1-C2, F4, E4-E6, F4-F6, and the canal loading and unloading area are the main emission areas of the pollution sources. Using online monitoring data, the research combined a PMF model, meteorological conditions, and corporate emissions information to achieve precise positioning of the pollution sources of VOCs in the industrial park, thus providing a basis for the supervision and management of corporate emissions in industrial parks.

[Spatio-Temporal Variations and Source Apportionment of Carbonaceous Species in PM2.5 Across Multiple Sampling Locations in the Chengdu Plain].

To understand the characteristics and sources of carbonaceous aerosols, one-year PM2.5 samples were analyzed for their organic carbon (OC) and elemental carbon (EC) content, following the thermal/optical transmission protocol in three cities[Deyang (DY), Chengdu (CD), and Meishan (MS)] in the Chengdu Plain. The observed annual average concentrations (µg·m-3) were in the following order:MS (15.8±9.6 OC and 6.6±5.3 EC) > CD (13.0±7.5 OC and 4.7±3.6 EC) > DY (9.6±6.1 OC and 3.4±2.6 EC). Organic matter (1.6OC) and EC was regarded as the total carbonaceous aerosols (TCA) amount, and the TCA/PM2.5 ratios at the three above-mentioned cities were 36%, 34%, and 30% respectively. The EC-trace method was used to estimate secondary organic carbon (SOC), which accounted for 38%, 46%, and 47% of total OC in MS, CD, and DY. Daily variations of OC and EC concentrations exhibited significant daily variations, with simultaneous peaks on Oct. 12th to 13th, 2013, Dec. 2nd to 7th, 2013, and mid-to-late Jan., 2014. The surging concentrations of K+ during the pollution period implied the contribution of biomass burning to heavy pollution. Six sources were resolved by the positive matrix factorization (PMF) model, whose contributions to the total carbon (TC) were:biomass burning (46%-56%), secondary aerosols (26%-38%), vehicle emission (9%-12%), fugitive dust (3%-4%), coal combustion (2%-3%), and industry emission (1%-2%). Biomass burning activities presented a significant influence on TC throughout the year, especially in autumn and winter.

[Characteristics of Ambient VOCs at the Shuangliu Site in Chengdu, China, During Summer and Autumn].

To better understand the characteristics and sources of ambient volatile organic compounds (VOCs) in a polluted area in Chengdu, which requires air pollution control, samples were collected hourly at the Shuangliu site from August 2016 to December 2016. Online gas chromatography/mass spectrometry and a flame ionization detector (GC/MS-FID) were used to analyze the mixing ratios and compositions of the VOCs. During the sampling period, the average mixing ratios of the VOCs were (45.15±43.74)×10-9. Alkanes contribute the most to the total volatile organic compounds (TVOCs), followed by aromatics (22%), halocarbon (17%), oxygenated volatile organic compounds (OVOCs; 15%), alkenes (9%), acetylene (7%), and acetonitrile (1%). Acetone, dichloromethane, acetylenes, ethylene, toluene, m/p-xylenes, propane, 1,2-dichloroethane, and methyl ethyl ketone are the dominant species. By calculating the OH loss rate, the chemical reactivity of the VOCs was estimated. Aromatics contribute the most to the total VOC reactivity, followed by alkenes. The most reactive species are styrene, m/p-xylenes, isoprene, and ethylene. Two biomass burning events were detected during the sampling period. The average mixing ratio of TVOCs is 57.65×10-9, which significantly increased during the national holiday. The mixing ratios of several C2-C5 alkenes, halocarbons, and OVOCs increased the most during the national holiday. The diurnal patterns of critical non-methane hydrocarbons (NMHCs) and OVOCs are consistent with the emission sources in this area. The VOC characteristics at the sampling site are mainly influenced by local industrial sources.

Particulate pollution in urban Chongqing of southwest China: Historical trends of variation, chemical characteristics and source apportionment.

Chongqing, the largest megacity in southwest China, faces serious aerosol pollution but lacks information on particle characteristics and its sources. Official data released by Chongqing Environmental Protection Bureau demonstrated that urban PM10 concentrations decreased remarkably from 150µgm-3 in 2000 to 90µgm-3 in 2012. However, only several peer-reviewed studies paid attention to local fine particle (PM2.5) pollution. In the study, PM2.5 samples were obtained and subjected to chemical analysis in an urban site of the city during 2012 to 2013. The annual mean PM10 and PM2.5 concentrations in urban Chongqing were 103.9±52.5 and 75.4±42.2µgm-3, respectively. PM2.5 showed a distinct seasonality of high concentration in winter and similar levels in other seasons. The average OC/EC (organic carbon/element carbon) ratio was 3.7 with more high-OC/EC ratio sources contribution in autumn and winter. The varying sources and formation mechanisms resulted in SO42- and NH4+ peaks in both summer and winter, whereas high nitrate concentration was only observed in winter. In the average mass closure, PM2.5 was composed of 23.0% SO42-, 11.7% NO3-, 10.9% NH4+, 30.8% OM (organic matter), 5.2% EC, 8.2% mineral dust, 0.6% TEO (trace elements), 1.0% Cl- and 1.1% K+, while exhibiting large seasonal variability. Using positive matrix factorization (PMF), six sources were apportioned in PM2.5: secondary inorganic aerosols, coal combustion, other industrial pollution, soil dust, vehicular emission, and metallurgical industry. The annual mean contribution of above sources to PM2.5 was 37.5, 22.0, 17.5, 11.0, 9.8 and 2.2%, respectively. Coal combustion was identified by As tracer and dominated the primary sources of PM2.5, while the two different industrial sources were characterized by Cr and Mo, Co, Ni, and Se, respectively. The study is of great importance in characterizing the historical trends, current chemical characteristics and sources of fine particles in urban Chongqing.

[Study on critical loads of sulfur and nitrogen in the Pearl River Delta].

Supported by the geographical information system (ArcGIS), critical loads and exceedances of critical loads of sulfur and nitrogen in the Pearl River Delta (PRD) were calculated using Steady-state Mass Balance method with current deposition data, vegetation data and soil data obtained by field sampling and laboratory analysis. Results showed that the present critical loads of sulfur were high in the eastern PRD and low in the west. Higher critical loads occurred in most of Huizhou, north-central Guangzhou, Dongguan and south Zhongshan. The critical loads of these regions were mostly larger than 15 keq x (hm2 x a)(-1). Regions with lower critical loads included most of Jiangmen, most of Zhaoqing and part of Shenzhen with critical loads less than 2 keq x (hm2 x a)(-1). Critical loads of nitrogen were mainly in the range of 1.0-2.5 keq x (hm2 x a)(-1) while values lower than 1.0 keq x (hm2 x a)(-1) were found in Zhaoqing. According to the results of critical load exceedances, in several regions the sulfur deposition exceeded the critical loads whereas in most regions the nitrogen deposition exceeded the critical loads. With the reduction of particulate concentrations in atmosphere in the future, critical loads of sulfur would decrease and sulfur depositions in most regions would exceed their critical loads. Therefore, the control over nitrogen deposition should be strengthened in the present situation and special attention should be paid to the control of sulfur deposition with the reduction of particulate concentrations in the future.

Characteristics and formation mechanism of a heavy air pollution episode caused by biomass burning in Chengdu, Southwest China.

To track the chemical characteristics and formation mechanism of biomass burning pollution, the hourly variations of meteorological factors and pollutant concentrations during a heavy pollution on 18-21 May, 2012 in Chengdu are presented in this study. The episode was the heaviest and most long-lasting pollution event in the historical record of Chengdu caused by a combination of stagnant dispersion conditions and enhanced PM2.5 emission from intensive biomass burning, with peak values surpassing 500 µg m(-3). The event was characterized by three nighttime peaks, relating to the burning practice and decreased boundary layer height at night. The prevailing northeasterly wind during nighttime preferentially brought more pollutants to the urban regions from northern suburbs of Chengdu, where dense fire spots were observed. Due to the obstruction of hilly topography and weak wind speed, minor regional features were reflected from the PM10 variations in nearby cities, whereas the long-distance transport of the plume impacted extensive regions in northern and eastern China. Carbon monoxide (CO) concentrations increased by more than 200%, while exceptionally high PM2.5 levels of 190.1 and 268.4 µg m(-3) on 17 May and 18 May, were observed and showed high correlation with CO (r=0.75). The relative contribution of biomass burning smoke to organic carbon was estimated from OC/EC ratios (organic carbon/elemental carbon) and elevated to 81.3% during the episode, indicating a significant impact on urban aerosol levels. The occurrence of high PM2.5/PM10 ratios (>0.80) and K(+)/EC ratios (>1.0), along with the increased carbonaceous concentrations and their fraction in PM2.5 (>40%) and high OC/EC ratios (about 8), could be used as immediate indicators for biomass burning pollution in cities. In addition, the heavy pollution involved a mixture of anthropogenic sources, reflected from the high SOR and NOR values and increases in the EFs (enrichment factors) of Mo, Zn, Cd, and Pb.

[Characteristics of haze and its impact factors in four megacities in China during 2006-2009].

The meteorological and environmental data including visibility, SO2, NO2 and PM10 were collected in four major megacities (Beijing, Shanghai, Guangzhou and Chengdu) in the years 2006-2009. Based on the data, seasonal and annual variations of the haze frequency and the key impact factors were discussed. The results indicated that the highest frequencies of haze occurred in summer, winter, spring and autumn for Beijing, Shanghai, Guangzhou and Chengdu, respectively. The trends of haze frequency decreased in Beijing and Guangzhou, while increased in Shanghai and Chengdu during the studied period. The PM10 concentration and relative humidity were the key factors for visibility degradation in the four megacities. The variation of visibility was sensitive to relative humidity in Beijing, to PM10 in Shanghai and Guangzhou, and to both in Chengdu.

Long-term trends and characteristics of visibility in two megacities in southwest China: Chengdu and Chongqing.

Visibility is a good indicator of air quality because it reflects the combined influences of atmospheric pollutants and synoptic processes. Trends in visibility and relationships with various factors in Chengdu and Chongqing, two megacities in southwest China, were analyzed using daily data from National Climatic Data Center and the Air Pollution Index (API) of the Ministry of Environmental Protection of China. Average annual visibility during the period of 1973-2010 was 8.1 +/- 3.9 in Chengdu and 6.2 +/- 4.3 km in Chongqing. PM10 dominates the reported primary pollutants in both cities, although concentrations have decreased from a high of 127.9 and 150 microg m3 before 2005 to 100.4 and 93.5 microg m(-3) in Chengdu and Chongqing, respectively. Low average visibility and extremely high levels of PM10 were observed in winter, whereas relative humidity had irregular and weak seasonal variations. Visibility in both cities has deteriorated in comparison to the 1960s and 1970s, mostly due-to the elevation of optical depth caused by anthropogenic pollution. Correlations and principal component analysis (PCA) were undertaken to determine the key factors affecting visibility. Visibility was only moderately correlated with PM10. In Chengdu, visibility displayed weak correlations with various factors, whereas visibility in Chongqing was most strongly related to relative humidity due to the atmospheric particulates in the region containing more hygroscopic components. PCA results further confirmed that high relative humidity and low wind speed increased the occurrence of low visibility events under high PM10 concentrations. Temperature and pressure, as indicators of weather systems, also played important roles in affecting visibility. Mathematical models of visibility prediction indicated that wind speed had the largest coefficients among all meteorological factors, and reductions in PM10 concentration only led to minor improvements in visibility.

[Optimization of PM10 monitoring network in Beijing].

PM10 monitoring network in Beijing was classified using a new technique, positive matrix factorization (PMF). And then the removal bias of each cluster was calculated by GIS system and sites with redundant information were identified. The daily average mass concentrations of PM10 from July 2007 to June 2008 were analyzed at 26 sites. The result showed that PM10 monitoring network of Beijing was separated into 10 clusters. Tongzhou, Yanqing, Miyunshuiku, Fangshan, and Pinggu formed five separate clusters. The five clusters with more than one site each were Cluster 4, which included sites Fengtaihuayuan, Fengtaiyungang, Mentougou, Haidianbeibuxinqu, and Shijingshan, located within the west developing urban area; Cluster 7, which included Dongchengdongsi, Dongchengtiantan, Xichengwanshouxigong, Xichengguanyuan, Chaoyangaotizhongxin, Chaoyangnongzhanguan, and Shunyi, located mainly within the developed area and the east developing area; Cluster 8, which included Daxingyizhuang, Daxinghuangcun, and Daxingyufa, located within the southern suburban industrial area; Cluster 9, which included Miyunzhen and Huairou, located within the north remote rural area; and Cluster 10, which included Haidianxiangshan, Changpingdingling, Haidianwanliu, and Changpingzhen, located within the northwest suburban area. All the 10 clusters had unique seasonal variations. According to the removal criteria, two scenarios were constructed. The criterion of scenario 1 was the uncertainty of the PM10 monitoring network, and the optimization result in which 12-18 sites should be retained was equal to the original monitoring network included 26 sites. The criterion of scenario 2 was two times of the uncertainty, and 10-13 sites needed to be retained.

[Assessing PM10 and SO2 networks using positive matrix factorization in Beijing city].

The aim of this study was to identify city areas with similar air pollution characteristics and determine which sites may be providing redundant information. Positive matrix factorization (PMF) was applied in this study to assess the mass concentrations of sulfur dioxide (SO2) and particulate matter with an aerodynamic diameter less than 10 microm (PM010), collected in the air quality monitoring network in the year of 2000. The analysis indicated that there were obviously seasonal variations for PM10 and SO2 in Beijing. The PM10 concentrations were higher in spring and lower in summer, but the SO2 concentrations were higher in winter and lower in summer. The results of the PMF showed that the sites of PM10 network in Beijing could be identified as three regions, which represented city areas characterized by the same specific air pollution. These three regions represented Gucheng site/Chegongzhuang site, Qianmen site/National Olympic Sports Center site/Tiantan site/Nongzhanguan site, and Ming Tombs site, respectively. Some sites in region 2 which included four sites may be redundant and can be removed. SO2 network can be divided into six regions including Chegongzhuang site/Qianmen site, Tiantan site/Nongzhanguan stie, Ming Tombs site, National Olympic Sports Center site, Dongsi site, and Gucheng site. It indicated that some sites in Beijing PM10 and SO2 monitoring networks might be redundant and could be removed or relocated to other areas.

[Modeling the vehicle pollution in the urban streets before and during the Beijing Olympic Games traffic control period].

In order to investigate the vehicle pollution situation in the streets in Beijing and the abatement during the Olympic Games, the OSPM model was applied to calculate the concentrations of PM10, CO, NO2 and O3 inside the urban streets of Beijing before and during the Olympic traffic controlling period in July, 2008. The modeled concentrations before the traffic control are 146 micog/m3, 3.83 mg/m3, 114.4 microg/m3 and 4.71 x 10(-1), while after the traffic control are 112 microg/m3, 3.16 mg/m3, 102.4 microg/m3 and 5.31 x 10(-9) , with the reduction rates of 23.4%, 20.5%, 10.5% and -12.5%, respectively. The research on these concentration changes and the daily variations of the pollutants reveals: the concentration of PM10 is most influenced by the traffic control; the concentration of CO presents the most similar daily variation with the traffic flow; the reduction of NO2 concentration is limited, indicating the influence of other factors other than the traffic emission; the concentration of O3 increases after the traffic control, which means the traffic management measures can not abate the O3 pollution in the street. Furthermore, the comparison between the calculation results in different types of street canyons reveals that the fleet composition and street geometry impact the concentration changes. In a word, the vehicle pollution inside the streets of Beijing before the traffic control is relatively serious, as the concentrations of PM10, CO and NO2, all approach or exceed the Grade II National Air Quality Standard; the traffic control measures take effect in reducing the primary pollutants, but the secondary pollutants may increase after the traffic control.

[Estimation of VOC emission from forests in China based on the volume of tree species].

Applying the volume data of dominant trees from statistics on the national forest resources, volatile organic compounds (VOC) emissions of each main tree species in China were estimated based on the light-temperature model put forward by Guenther. China's VOC emission inventory for forest was established, and the space-time and age-class distributions of VOC emission were analyzed. The results show that the total VOC emissions from forests in China are 8565.76 Gg, of which isoprene is 5689.38 Gg (66.42%), monoterpenes is 1343.95 Gg (15.69%), and other VOC is 1532.43 Gg (17.89%). VOC emissions have significant species variation. Quercus is the main species responsible for emission, contributing 45.22% of the total, followed by Picea and Pinus massoniana with 6.34% and 5.22%, respectively. Southwest and Northeast China are the major emission regions. In specific, Yunnan, Sichuan, Heilongjiang, Jilin and Shaanxi are the top five provinces producing the most VOC emissions from forests, and their contributions to the total are 15.09%, 12.58%, 10.35%, 7.49% and 7.37%, respectively. Emissions from these five provinces occupy more than half (52.88%) of the national emissions. Besides, VOC emissions show remarkable seasonal variation. Emissions in summer are the largest, accounting for 56.66% of the annual. Forests of different ages have different emission contribution. Half-mature forests play a key role and contribute 38.84% of the total emission from forests.

[Development of vehicular emission inventory in China].

The vehicular emission inventories with high spatial resolution of 40km x 40km are developed using GIS technique based on the statistic data from yearbooks about vehicles and roads at provincial level in China for the year 2002, and on the emission factors calculated by COPERT III model for each category of vehicles in urban, rural and highway traffic. The results show that the emissions of CO, NOx, NMVOC and PM10 are 28.15, 3.05, 4.61 and 1.11 million tons, respectively, principally from motorcycles and gasoline passenger cars. The emissions concentrate on the developed areas and those from 10.8%, 2.2%, 9.7% and 5.3% of country acreage account for 84% of CO, 55% of NMVOC, 48% of NOx, 48% of PM10 emissions, respectively. The emissions in the east of China and coastal areas are higher than those in the west and hinterland areas. The emission source strengths in the Yangtze River Delta, the Pearl River Delta and Beijing & Tianjin area are the highest.