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1.
The Asian Dust Aerosol Model 2 with the MM5 meteorological model has been employed to estimate the dust emission, dust concentration, and wet and dry deposition of dust in the Asian region for the month of March in 2010. It is found that the model simulates quite reasonably the dust (PM10) concentrations both in the dust source region and the downstream region of Korea. The starting and ending times of most dust events and their peak concentration occurrence times are well simulated. The monthly mean maximum surface dust concentration (PM10) is found to be 267???g?m?3 in the domain of central northern China (CNC). Monthly total maximum dust emission of more than 32?t km?2 and that of deposition of more than 25.4?t km?2 (dry deposition of 24?t km?2 and wet deposition of 1.4?t km?2) are found to occur in the domain CNC, whereas the monthly mean minimum surface dust concentration (PM10) is found to be 0.2???g?m?3 in the domain of the Tibetan Plateau, where the monthly total dust emission (4?kg?km?2) and the monthly total dust deposition (9?kg?km?2) are found to be minimum. This monthly total dust deposition of 9?kg?km?2 (dry deposition of 7?kg?km?2 and wet deposition of 2?kg?km?2) is as large as 2.25 times of that of emission (4?kg?km?2), suggesting net dust influx toward the Tibetan Plateau from the surrounding dust source regions. It is also found that the ratio of the total dust deposition to the total dust emission in the source region increases toward the downstream direction from 0.4 in the upstream source region of Taklimakan to 0.80 in the downstream source region of northeastern China. More than 90% of the total dust deposition is found to be contributed by dry deposition due to the lack of precipitation in the dust source region. The monthly mean dust concentration (PM10) is found to decrease with distance away from the dust source region. The monthly mean dust concentration of 62???g?m?3 over the Yellow Sea (YES) decreases to 4.3???g?m?3 over the Northwestern Pacific Ocean (NWP). The monthly total dust deposition in the downstream region is also found to decrease away from the source region from 2.33?t km?2 (dry deposition of 1.36?t km?2 and wet deposition of 0.97?t km?2) over the domain YES to 1.45?t km?2 (dry deposition of 0.16?t km?2 and wet deposition of 1.30?t km?2) over the domain NWP. A large amount of the total dust deposition over the seas is contributed by wet deposition (more than 90%), causing a small decreasing rate of the total dust deposition with distance from the source region. The estimated dust deposition could adversely impact the eco-environmental system significantly in the downstream regions of the Asian dust source region, especially over the seas.  相似文献   

2.
The Asian dust forecasting model, Mongolian Asian Dust Aerosol Model (MGLADAM), has been operated by the National Agency for Meteorology and Environmental Monitoring of Mongolia since 2010, for the forecast of Asian dust storms. In order to evaluate the performance of the dust prediction model, we simulated Asian dust events for the period of spring 2011. Simulated features were compared with observations from two sites in the dust source region of the Gobi desert in Mongolia, and in the downstream region in Korea. It was found that the simulated wind speed and friction velocity showed a good correlation with observations at the Erdene site (one of the sites in the Gobi desert). The results show that the model is proficient in the simulation of dust concentrations that are within the same order of magnitude and have similar start and end times, compared with PM10 observed at two monitoring sites in the Gobi regions. Root Mean Square Error (RMSE) of the dust simulation ranges up to 200 μg m?3 because of the high concentrations in source regions, which is three times higher than that in the downstream region. However, the spatial pattern of dust concentration matches well with dust reports from synoptic observation. In the downwind regions, it was found that the model simluated all reported dust cases successfully. It was also found that the RMSE in the downwind region increased when the model integration time increased, but that in the source regions did not show consistent change. It suggests that MGLADAM has the potential to be used as an operational dust forecasting model for predicting major dust events over the dust source regions as well as predicting transported dust concentrations over the downstream region. However, it is thought that further improvement in the emission estimation is necessary, including accurate predictions in surface and boundary layer meteorology. In the downwind regions, background PM10 concentration is considerably affected by other aerosol species, suggesting that a consideration of anthropogenic pollutants will be required for accurate dust forecasting.  相似文献   

3.
The operational Asian Dust Aerosol Model (ADAM)1 in Korea Meteorological Administration has been modified to the ADAM2 model to be used as an operational forecasting model all year round not only in Korea but also in the whole Asian domain (70-160°E and 5-60°N) using the routinely available World Meteorological Organization (WMO) surface reporting data and the Spot/vegetation Normalized Difference Vegetation Index (NDVI) data for the period of 9 years from 1998 to 2006. The 3-hourly reporting WMO surface data in the Asian domain have been used to re-delineate the Asian dust source region and to determine the temporal variation of the threshold wind speed for the dust rise. The dust emission reduction factor due to vegetation in different surface soil-type regions (Gobi, sand, loess, and mixed soil) has been determined with the use of NDVI data. It is found that the threshold wind speed for the dust rise varies significantly with time (minimum in summer and maximum in winter) and surface soil types with the highest threshold wind speed of 8.0 m?s?1 in the Gobi region and the lowest value of 6.0 m?s?1 in the loess region. The statistical analysis of the spot/vegetation NDVI data enables to determine the emission reduction factor due to vegetation with the free NDVI value that is the NDVI value without the effect of vegetation and the upper limit value of NDVI for the dust rise in different soil-type regions. The modified ADAM2 model has been implemented to simulate two Asian dust events observed in Korea for the periods from 31 March to 2 April 2007 (a spring dust event) and from 29 to 31 December 2007 (a winter dust event) when the observed PM10 concentration at some monitoring sites in the source region exceeds 9,000 μg m?3. It is found that ADAM2 model successfully simulates the observed high dust concentrations of more than 8,000 μg m?3 in the dust source region and 600 μg m?3 in the downstream region of Korea. This suggests that ADAM2 has a great potential for the use of an operational Asian dust forecast model in the Asian domain.  相似文献   

4.
Continuous observations of mass concentration and elemental composition of aerosol particles （PM2.5） were conducted at Tongyu, a semi-arid site in Northeast China in the spring of 2006. The average mass concentration of PM2.5 at Tongyu station was 260.9±274.4 μg m^-3 during the observation period. Nine dust events were monitored with a mean concentration of 528.0±302.7 μgm^-3. The PM2.5 level during non- dust storm （NDS） period was 111.65±63.37 μg m^-3. High mass concentration shows that fine-size particles pollution was very serious in the semi-arid area in Northeast China. The enrichment factor values for crust elements during the dust storm （DS） period are close to those in the NDS period, while the enrichment factor values for pollution elements during the NDS period are much higher than those in the DS period, showing these elements were from anthropogenic sources. The ratios of dust elements to Fe were relative constant during the DS period. The Ca/Fe ratio in dust aerosols at Tongyu is remarkably different from that observed in other source regions and downwind regions. Meteorological analysis shows that dust events at Tongyu are usually associated with dry, low pressure and high wind speed weather conditions. Air mass back-trajectory analysis identified three kinds of general pathways were associated with the aerosol particle transport to Tongyu, and the northwest direction pathway was the main transport route.  相似文献   

5.
A strong dust-storm (23–25 April, 2009) occurred in the provinces of Inner Mongolia, Gansu, and Shanxi, North China. Cities along the storm path (from north to south: Xi’ning, Lanzhou, Chengdu, Changsha, and Guangzhou) all experienced a sharp increase in particle matter (PM10) concentration. This is the first case that an Asian dust storm hit Guangzhou in Southern China. The impacts of dust storm on the characteristics of PM were investigated using samples collected in Guangzhou during 27–29 April, 2009. In addition, the mass concentration and chemical composition during a normal non-dust period (12–14 May, 2009) were compared with those in dust period. The results show that the concentration of PM10 during the dust episode (0.231 mg m?3) was twice higher than that in the non-dust episode (0.103 mg m?3). Chemical analysis showed that concentrations of metal elements, enrichment factors of metal elements, and soluble ions during the dust episode were very different from those of non-dust. The total concentration of metal elements content in PM10 was 53.5 μg m?3 in the dust episode, which is about two times higher than that in non-dust episode (28.5 μg m?3). Increases in concentrations of Na, Ti, Zn, Cu, and Cr ranged from zero to 100% during the dust episode. However, the enrichment factors in non-dust episode were higher than that in dust-storm period, indicating that the above five chemicals originated mainly from local sources in Guangzhou. The concentrations of K, Mg, Al, Fe, Mn, V, and Co increased by over 100% in the dust episode, indicating their origins of remote sources. In the dust period, some water-soluble ions increased in PM10, but the main components in PM10 were SO4 ?, NO3 ? and NH4 +. At last, we assessed the sources of dusts by analyzing synoptic situation and back trajectories of air mass in Guangzhou, and demonstrated that the main source of the dust storm was from Mongolia.  相似文献   

6.
This study investigated meteorological, physical, and chemical characteristics of 2 severe Hwangsa (Asian dust, maximum average of PM10 above 1000 μg m?3) observed in Seoul, the capital city of Korea, during 30～31st May, 2008 (DSS2008) and 25～26th December, 2009 (DSS2009). DSS2008 and DSS2009 had a same source region and route. However, they have different meteorological conditions. DSS2009 had a shorter travel time from the source region to Korea and shorter duration time in Korea than DSS2008 due to a strong winter Siberian anticyclone. One of DSS2008 sample was affected by not only Asian dust but also a long-range transported haze due to consecutive influx after low pressure passed while DSS2009 sample collected only dust aerosol. For both cases, the mass concentration of coarse particles (PM10-1) increased by 3～14 times compared to that during non Asian dust period, however, that of fine particles (PM1) increased only in DSS2008. For DSS2008 water-soluble ion balance between anions and cations in fine mode was close to 1:1 while cations were higher than anions in coarse mode. NH4 + and Ca2+ were found to be the main contributing factors for the neutralization. Cl? loss was observed about 60% indicating an active interaction of Na+ with pollutants. Reconstruction of chemical compositions showed relatively high concentrations of secondary pollutants (NH4NO3 and (NH4)2SO4), CaCO3, and Ca(NO3)2 compared to that during non Asian dust period. DSS2009 exhibited the typical characteristics of Asian dust having a high concentration of Ca2+ with higher equivalent concentration of cations than anions in all size bins. Cl? loss was hardly observed. The secondary pollutants were lower than that of non Asian dust cases. The result of reconstruction of ionic components indicated the CaCO3 derived from soil particle, CaSO4, and Ca (NO3)2 were dominant in DSS2009.  相似文献   

7.
气溶胶质量密度是气溶胶重要的参数,它影响着大气中复杂的化学反应,也与气溶胶的传输过程和空间分布息息相关.基于MERRA-2再分析资料提供的气溶胶柱质量密度数据,研究了我国塔里木盆地1980—2018年长时间序列的沙尘气溶胶柱质量密度的时空分布特征.结果表明,沙尘气溶胶和沙尘PM2.5气溶胶柱质量密度有很大的变化范围,平均值分别为0.33和0.086 g/m2,同时具有明显的年际、月和季节变化特征.沙尘气溶胶和沙尘PM2.5气溶胶柱质量密度的年平均值在0.24~0.41和0.06~0.11 g/m2范围内变化;春季最大,其平均值分别为0.47和0.12 g/m2,冬季最小,其平均值分别为0.13和0.04 g/m2;月平均值最大出现在5月,分别为0.57和0.14 g/m2,最小在1月,分别为0.1和0.03 g/m2.  相似文献   

8.
Results are presented of monitoring measurements of the mass concentration of PM10 (particles with the size of less than 10 μm) and PM2.5 (less than 2.5 μm) fine-dispersed aerosol fractions at the Sainshand and Zamyn-Üüd stations located in the Gobi Desert of Mongolia. Revealed are the annual variations of the mass concentration of PM10 and PM2.5 fine-dispersed aerosol fractions at these stations in 2008. The maximum values of monthly mean concentration during the year were observed in May in the period of dust storms. On the days with the steady calm weather, the mass concentrations of PM10 and PM2.5 varied within 5–8 μg/m3 (PM10) and 3–5 μg/m3 (PM2.5) at the Sainshand station. During the dust storms, the maximum values of concentration exceeded 1400 μg/m3 (PM10) and 380 μg/m3 (PM2.5) that is by 28 (PM10) and 15 (PM2.5) times higher than the maximum permissible concentration for the European Union. Results are given of studying the frequency and duration of dust storms in recent 20 years (1991–2010) in the Eastern Gobi Desert.  相似文献   

9.

Size-segregated aerosol particles were collected using a high volume MOUDI sampler at a coastal urban site in Xiamen Bay, China, from March 2018 to June 2020 to examine the seasonal characteristics of aerosol and water-soluble inorganic ions (WSIIs) and the dry deposition of nitrogen species. During the study period, the annual average concentrations of PM1, PM2.5, PM10, and TSP were 14.8?±?5.6, 21.1?±?9.0, 35.4?±?14.2 μg m?3, and 45.2?±?21.3 μg m?3, respectively. The seasonal variations of aerosol concentrations were impacted by the monsoon with the lowest value in summer and the higher values in other seasons. For WSIIs, the annual average concentrations were 6.3?±?3.3, 2.1?±?1.2, 3.3?±?1.5, and 1.6?±?0.8 μg m?3 in PM1, PM1-2.5, PM2.5–10, and PM>10, respectively. In addition, pronounced seasonal variations of WSIIs in PM1 and PM1-2.5 were observed, with the highest concentration in spring-winter and the lowest in summer. The size distribution showed that SO42?, NH4+ and K+ were consistently present in the submicron particles while Ca2+, Mg2+, Na+ and Cl? mainly accumulated in the size range of 2.5–10 μm, reflecting their different dominant sources. In spring, fall and winter, a bimodal distribution of NO3? was observed with one peak at 2.5–10 μm and another peak at 0.44–1 μm. In summer, however, the fine mode peak disappeared, likely due to the unfavorable conditions for the formation of NH4NO3. For NH4+ and SO42?, their dominant peak at 0.25–0.44 μm in summer and fall shifted to 0.44–1 μm in spring and winter. Although the concentration of NO3–N was lower than NH4–N, the dry deposition flux of NO3–N (35.77?±?24.49 μmol N m?2 d?1) was much higher than that of NH4–N (10.95?±?11.89 μmol N m?2 d?1), mainly due to the larger deposition velocities of NO3–N. The contribution of sea-salt particles to the total particulate inorganic N deposition was estimated to be 23.9—52.8%. Dry deposition of particulate inorganic N accounted for 0.95% of other terrestrial N influxes. The annual total N deposition can create a new productivity of 3.55 mgC m?2 d?1, accounting for 1.3–4.7% of the primary productivity in Xiamen Bay. In light of these results, atmospheric N deposition could have a significant influence on biogeochemistry cycle of nutrients with respect to projected increase of anthropogenic emissions from mobile sources in coastal region.

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10.

This study presents the chemical composition (carbonaceous and nitrogenous components) of aerosols (PM2.5 and PM10) along with stable isotopic composition (δ13C and δ15N) collected during winter and the summer months of 2015–16 to explore the possible sources of aerosols in megacity Delhi, India. The mean concentrations (mean?±?standard deviation at 1σ) of PM2.5 and PM10 were 223?±?69 µg m?3 and 328?±?65 µg m?3, respectively during winter season whereas the mean concentrations of PM2.5 and PM10 were 147?±?22 µg m?3 and 236?±?61 µg m?3, respectively during summer season. The mean value of δ13C (range: ??26.4 to ??23.4‰) and δ15N (range: 3.3 to 14.4‰) of PM2.5 were ??25.3?±?0.5‰ and 8.9?±?2.1‰, respectively during winter season whereas the mean value of δ13C (range: ??26.7 to ??25.3‰) and δ15N (range: 2.8 to 11.5‰) of PM2.5 were ??26.1?±?0.4‰ and 6.4?±?2.5‰, respectively during the summer season. Comparison of stable C and N isotopic fingerprints of major identical sources suggested that major portion of PM2.5 and PM10 at Delhi were mainly from fossil fuel combustion (FFC), biomass burning (BB) (C-3 and C-4 type vegitation), secondary aerosols (SAs) and road dust (SD). The correlation analysis of δ13C with other C (OC, TC, OC/EC and OC/WSOC) components and δ15N with other N components (TN, NH4+ and NO3?) are also support the source identification of isotopic signatures.

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11.
Inhalable particles (PM10), with aerodynamic equivalent diameters that are generally 10 micrometers or smaller, are basic pollutants in many areas, especially in northern China, and thus the pollution from PM10 inhalable particulate matter is a growing concern for public health. Independent long-term observations are necessary to evaluate the efficacy of PM10 reduction actions. Variations in the PM10 concentration from 2006 to 2017 at an observation station (NJ) in Beijing were recorded and analyzed. The average value ±1 standard deviation of daily mean PM10 concentrations was 138.8 ±96.1 μg m-3 for 1307 days (accounting for 34.7% of the total days), showing PM10 concentration exceeding the National Ambient Air Quality Standard (NAAQS) 24-h average of 150 μg m-3. Particulate concentration depended upon various meteorological conditions as also observed in this work: at low wind speed (<4 m s-1), the concentrations of PM10 revealed a downward trend with -19 μg m-3 per unit of wind speed, but when wind speed rose (>4 m s-1), the values increased by 49 μg m-3 per unit of wind speed. In Beijing, air masses from northwest China, especially from the Gobi Desert and other desert areas, had net contributions to long-range transport of natural dust, enhancing the PM10 concentrations by up to 29%. Overall, PM10 mass concentration showed a significant downward trend with -8.0 μg/m3/yr from 2006 to 2017. Although with higher fluctuations in recorded data, similar downward trends derived from the ) in 2017 still exceeded the NAAQS standard. The results showed that there is still a long way to go to reduce PM10 in Beijing.  相似文献   

12.
Ambient concentrations of organic carbon (OC), elemental carbon (EC) and water soluble inorganic ionic components (WSIC) of PM10 were studied at Giridih, Jharkhand, a sub-urban site near the Indo Gangatic Plain (IGP) of India during two consecutive winter seasons (November 2011–February 2012 and November 2012–February 2013). The abundance of carbonaceous and water soluble inorganic species of PM10 was recorded at the study site of Giridih. During winter 2011–12, the average concentrations of PM10, OC, EC and WSIC were 180.2?±?46.4; 37.2?±?6.2; 15.2?±?5.4 and 18.0?±?5.1 μg m?3, respectively. Similar concentrations of PM10, OC, EC and WSIC were also recorded during winter 2012–13. In the present case, a positive linear trend is observed between OC and EC at sampling site of Giridih indicates the coal burning, as well as dispersed coal powder and vehicular emissions may be the source of carbonaceous aerosols. The principal components analysis (PCA) also identifies the contribution of coal burning? +?soil dust, vehicular emissions?+?biomass burning and seconday aerosol to PM10 mass concentration at the study site. Backward trajectoy and potential source contributing function (PSCF) analysis indicated that the aerosols being transported to Giridih from upwind IGP (Punjab, Haryana, Uttar Pradesh and Bihar) and surrounding region.  相似文献   

13.
This study elucidates the characteristics of ambient PM2.5 (fine) and PM1 (submicron) samples collected between July 2009 and June 2010 in Raipur, India, in terms of water soluble ions, i.e. Na+, NH 4 + , K+, Mg2+, Ca2+, Cl?, NO 3 ? and SO 4 2? . The total number of PM2.5 and PM1 samples collected with eight stage cascade impactor was 120. Annual mean concentrations of PM2.5 and PM1 were 150.9?±?78.6 μg/m3 and 72.5?±?39.0 μg/m3, respectively. The higher particulate matter (PM) mass concentrations during the winter season are essentially due to the increase of biomass burning and temperature inversion. Out of above 8 ions, the most abundant ions were SO 4 2? , NO 3 ? and NH 4 + for both PM2.5 and PM1 aerosols; their average concentrations were 7.86?±?5.86 μg/m3, 3.12?±?2.63 μg/m3 and 1.94?±?1.28 μg/m3 for PM2.5, and 5.61?±?3.79 μg/m3, 1.81?±?1.21 μg/m3 and 1.26?±?0.88 μg/m3 for PM1, respectively. The major secondary species SO 4 2? , NO 3 ? and NH 4 + accounted for 5.81%, 1.88% and 1.40% of the total mass of PM2.5 and 11.10%, 2.68%, and 2.48% of the total mass of PM1, respectively. The source identification was conducted for the ionic species in PM2.5 and PM1 aerosols. The results are discussed by the way of correlations and principal component analysis. Spearman correlation indicated that Cl? and K+ in PM2.5 and PM1 can be originated from similar type of sources. Principal component analysis reveals that there are two major sources (anthropogenic and natural such as soil derived particles) for PM2.5 and PM1 fractions.  相似文献   

14.
参考AP-42方法的采样规范（USEPA,2011）,对武汉市13个城区的不同类型道路采集了137个扬尘样,并记录采样面积、车流情况、车道状况、地理位置、周围环境以及气象数据要素信息,得到了不同类型道路的积尘负荷,估算了其扬尘排放因子和排放量.结果表明：武汉总城区尘负荷由大到小顺序为支路 > 次干道 > 主干道 > 快速路,其中支路平均尘负荷为2.396 g/m2,快速路为0.852 g/m2,远城区平均尘负荷是主城区平均尘负荷的2倍左右.各类型道路不同粒径范围的道路交通扬尘排放因子大小顺序为支路 > 次干路 > 主干路 > 高速路,与尘负荷大小趋势一致.2016年道路交通扬尘源TSP的年排放量为156 931.4 t,PM10的年排放量为39 868.7 t,PM2.5的年排放量为11 574.8 t,其不确定性范围分别为-24.7%~31.4%、-31.3%~32.9%、-31.8%~30.5%.其中主干道扬尘排放量最大,其TSP、PM10和PM2.5的年排放量分别为64 447.1、16 372.9和4 753.4 t.  相似文献   

15.

Pre and Post-Monsoon levels of ambient SO2, NO2, PM2.5 and the trace metals Fe, Cu, etc. were measured at industrial and residential regions of the Kochi urban area in South India for a period of two years. The mean PM2.5, SO2 and NO2 concentrations across all sites were 38.98?±?1.38 µg/m3, 2.78?±?0.85 µg/m3 and 11.90?±?4.68 µg/m3 respectively, which is lower than many other Indian cities. There was little difference in any on the measured species between the seasons. A few sites exceeded the NAAQS (define acronym and state standard) and most of the sites exceeded WHO (define acronym and state standard) standard for PM2.5. The average trace metal concentrations (ng/m3) were found to be Fe (32.58)?>?Zn (31.93)?>?Ni (10.13)?>?Cr (5.48)?>?Pb (5.37)?>?Cu (3.24). The maximum concentration of trace metals except Pb were reported in industrial areas. The enrichment factor, of metals relative to crustal material, indicated anthropogenic dominance over natural sources for the trace metal concentration in Kochi’s atmosphere. This work demonstrates the importance of air quality monitoring in this area.

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16.
In each year, Dust and Sandstorms (DSSs) triggered by cold air masses enhance particle concentration over large areas in China during spring and winter. In this paper, daily Air Pollution Index (API) of 113 major cities in China during dust events was analyzed to present the influence of DSSs on urban air quality. From 2005 to 2010, a total of 93 dust events were identified, on average there are approximately 16 dust events in a year. The number of total polluted days caused by DSSs in 113 major cities ranged from 147 to 546 each year, with maximum in 2010 and minimum in 2007. The number of total heavily polluted days caused by DSSs in major cities ranged from 14 to 78 each year, with maximum in 2010 and minimum in 2005. DSSs affected major cities most severely during March to May. Furthermore, a typical DSS observed from 26 to 31 May 2008 was described in terms of meteorological features and PM10 concentration as well as API levels of 113 major cities. This event lead to high PM10 concentration and low visibility over major cities, with maximum daily PM10 concentration of 1511 μg m?3 in Chifeng on 28 May, which was directly caused by strong wind in front of surface high pressure system passing through sand source areas in Mongolia and North China. The most severe pollution occurred on 29 May, with 38 cities polluted and 7 cities heavily polluted.  相似文献   

17.
PM10 samples were collected to characterize the seasonal and annual trends of carbonaceous content in PM10 at an urban site of megacity Delhi, India from January 2010 to December 2017. Organic carbon (OC) and elemental carbon (EC) concentrations were quantified by thermal-optical transmission (TOT) method of PM10 samples collected at Delhi. The average concentrations of PM10, OC, EC and TCA (total carbonaceous aerosol) were 222?±?87 (range: 48.2–583.8 μg m?3), 25.6?±?14.0 (range: 4.2–82.5 μg m?3), 8.7?±?5.8 (range: 0.8–35.6 μg m?3) and 54.7?±?30.6 μg m?3 (range: 8.4–175.2 μg m?3), respectively during entire sampling period. The average secondary organic carbon (SOC) concentration ranged from 2.5–9.1 μg m?3 in PM10, accounting from 14 to 28% of total OC mass concentration of PM10. Significant seasonal variations were recorded in concentrations of PM10, OC, EC and TCA with maxima during winter and minima during monsoon seasons. In the present study, the positive linear trend between OC and EC were recorded during winter (R2?=?0.53), summer (R2?=?0.59) and monsoon (R2?=?0.78) seasons. This behaviour suggests the contribution of similar sources and common atmospheric processes in both the fractions. OC/EC weight ratio suggested that vehicular emissions, fossil fuel combustion and biomass burning could be the major sources of carbonaceous aerosols of PM10 at the megacity Delhi, India. Trajectory analysis indicates that the air mass approches to the sampling site is mainly from Indo Gangetic plain (IGP) region (Uttar Pradesh, Haryana and Punjab etc.), Thar desert, Afghanistan, Pakistan and surrounding areas.  相似文献   

18.
Zhang  Xiaoyu  Ji  Guixiang  Peng  Xiaowu  Kong  Lingya  Zhao  Xin  Ying  Rongrong  Yin  Wenjun  Xu  Tian  Cheng  Juan  Wang  Lin 《Journal of Atmospheric Chemistry》2022,79(2):101-115

In this study, 123 PM2.5 filter samples were collected in Wuhan, Hubei province from December 2014 to November 2015. Water- soluble inorganic ions (WSIIs), elemental carbon (EC), organic carbon (OC) and inorganic elements were measured. Source apportionment and back trajectory was investigated by the positive matrix factorization (PMF) model and the hybrid single particle lagrangian integrated trajectory (HYSPLIT) model, respectively. The annual PM2.5 concentration was 80.5?±?38.2 μg/m3, with higher PM2.5 in winter and lower in summer. WSIIs, OC, EC, as well as elements contributed 46.8%, 14.8%, 6.7% and 8% to PM2.5 mass concentration, respectively. SO42?, NO3? and NH4+ were the dominant components, accounting for 40.2% of PM2.5 concentrations. S, K, Cl, Ba, Fe, Ca and I were the main inorganic elements, and accounted for 65.2% of the elemental composition. The ratio of NO3?/SO42? was 0.86?±?0.72, indicating that stationary sources play dominant role on PM2.5 concentration. The ratio of OC/EC was 2.9?±?1.4, suggesting the existence of secondary organic carbon (SOC). Five sources were identified using PMF model, which included secondary inorganic aerosols (SIA), coal combustion, industry, vehicle emission, fugitive dust. SIA, coal combustion, as well as industry were the dominant contributors to PM2.5 pollution, accounting for 34.7%, 20.5%, 19.6%, respectively.

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19.

In this study we present the seasonal chemical characteristics and potential sources of PM10 at an urban location of Delhi, India during 2010?2019. The concentrations of carbonaceous aerosols [organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC) and water insoluble organic carbon (WIOC)] and elements (Al, Fe, Ti, Cu, Zn, Mn, Pb, Cr, F, Cl, Br, P, S, K, As, Na, Mg, Ca, B, Ni, Mo, V, Sr, Zr and Rb) in PM10 were estimated to explore their possible sources. The annual average concentration (2010–2019) of PM10 was computed as 227?±?97 µg m?3 with a range of 34?734 µg m?3. The total carbonaceous aerosols in PM10 was accounted for 22.5% of PM10 mass concentration, whereas elements contribution to PM10 was estimated to be 17% of PM10. The statistical analysis of OC vs. EC and OC vs. WSOC of PM10 reveals their common sources (biomass burning and/or fossil fuel combustion) during all the seasons. Enrichment factors (EFs) of the elements and the relationship of Al with other crustal metals (Fe, Ca, Mg and Ti) of PM10 indicates the abundance of mineral dust over Delhi. Principal component analysis (PCA) extracted the five major sources [industrial emission (IE), biomass burning?+?fossil fuel combustion (BB?+?FFC), soil dust, vehicular emissions (VE) and sodium and magnesium salts (SMS)] of PM10 in Delhi, India. Back trajectory and cluster analysis of airmass parcel indicate that the pollutants approaching to Delhi are mainly from Pakistan, IGP region, Arabian Sea and Bay of Bengal.

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20.
Surface ozone (O3) and fine particulate matter (PM2.5) are dominant air pollutants in China. Concentrations of these pollutants can show significant differences between urban and nonurban areas. However, such contrast has never been explored on the country level. This study investigates the spatiotemporal characteristics of urban-to-suburban and urban-to-background difference for O3 (Δ[O3]) and PM2.5 (Δ[PM2.5]) concentrations in China using monitoring data from 1171 urban, 110 suburban, and 15 background sites built by the China National Environmental Monitoring Center (CNEMC). On the annual mean basis, the urban-to-suburban Δ[O3] is ?3.7 ppbv in Beijing–Tianjin–Hebei, 1.0 ppbv in the Yangtze River Delta, ?3.5 ppbv in the Pearl River Delta, and ?3.8 ppbv in the Sichuan Basin. On the contrary, the urban-to-suburban Δ[PM2.5] is 15.8, ?0.3, 3.5 and 2.4 μg m?3 in those areas, respectively. The urban-to-suburban contrast is more significant in winter for both Δ[O3] and Δ[PM2.5]. In eastern China, urban-to-background differences are also moderate during summer, with ?5.1 to 6.8 ppbv for Δ[O3] and ?0.1 to 22.5 μg m?3 for Δ[PM2.5]. However, such contrasts are much larger in winter, with ?22.2 to 5.5 ppbv for Δ[O3] and 3.1 to 82.3 μg m?3 for Δ[PM2.5]. Since the urban region accounts for only 2% of the whole country’s area, the urban-dominant air quality data from the CNEMC network may overestimate winter [PM2.5] but underestimate winter [O3] over the vast domain of China. The study suggests that the CNEMC monitoring data should be used with caution for evaluating chemical models and assessing ecosystem health, which require more data outside urban areas.  相似文献   

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