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10.1016/j.scitotenv.2014.08.008
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Fig. 6. Annual variation of (a) sulfate $({\mathsf{S}}0_{4}^{2-})$ (red dots) and sulfur dioxide $(S0_{2})$ (black dot), and (b) nitrate $\left(\mathsf{N O}_{3}^{-}\right)$ (red dot) and nitrogen oxide $\left(\mathrm{NO_{x}}\right)$ (black dot) in fall and winter from 2007 to 2011.
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Fig. 7. Annual variation of (a) elemental carbon (EC) and (b) organic carbon (OC) in fall and winter from 2007 to 2011.
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Fig. 8. Annual variation of (a) primary organic carbon (POC) and (b) secondary organic carbon (SOC) in fall and winter from 2007 to 2011.
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Trends of ambient fine particles and major chemical components in the Pearl River Delta region: Observation at a regional background site in fall and winter Xiaoxin Fu a,b, Xinming Wang a,⁎, Hai Guo b,⁎⁎, Kalam Cheung b, Xiang Ding a, Xiuying Zhao a, Quanfu He a, Bo Gao , Zhou Zhang , Tengyu Liu , Yanli Zhang a State K...
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10.1016/j.envpol.2017.10.123
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Fig. 1. Layout of sampling points of BTH region.
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Fig. 2. Temporal and spatial variations of mass concentration of $\mathrm{PM}_{2.5}$ in BTH region.
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Table 1 Seasonal average mass concentration of elements for $\mathrm{PM}_{2.5}$ in BTH region $(\upmu\mathrm{g}/\up m^{3})$ .
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Fig. 3. Enrichment factor of elements for $\mathrm{PM}_{2.5}$ at (a) BJ, (b) TJ, (c) LF and (d) BD sampling sites.
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Fig. 4. Seasonal distribution of WSIIs for $\mathrm{PM}_{2.5}$ at (a) BJ, (b) TJ, (c) LF and (d) BD sampling sites.
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Fig. 5. Mass concentration of OC, EC, and the ratio of OC/EC for $\mathrm{PM}_{2.5}$ in BTH region.
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Fig. 6. Seasonal correlation of OC and EC for $\mathrm{PM}_{2.5}$ at BJ site.
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Fig. 7. Mass balance of $\mathrm{PM}_{2.5}$ at BTH region.
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Table 2 PMF factor profiles for $\mathrm{PM}_{2.5}$ at BJ, TJ, LF, and BD sampling site.
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Temporal-spatial characteristics and source apportionment of PM2.5 as well as its associated chemical species in the Beijing-Tianjin-Hebei region of China Jiajia Gao a, b, Kun Wang a, b, Yong Wang a, c, Shuhan Liu a, c, Chuanyong Zhu a, d, Jiming Hao e, Huanjia Liu a, c, Shenbing Hua a, c, Hezhong Tian a, c, e, \* a St...
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Fig. 1. Scatter plots of major water-soluble ions from AIM and Filter-based methods.
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Table 1 Concentrations of water-soluble ions (mean concentrations $\pm$ standard deviation (SD)) in four seasons in Jinan $\left(\upmu\mathrm{g}\:\mathsf{m}^{-3}\right)$ .
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Table 2 Mass concentrations of $\mathsf{P M}_{2.5}$ and the major chemical components in Jinan and other cities over the world $(\upmu\mathrm{g}\textrm{m}^{-3}$ ).
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Fig. 2. Seasonal variations of SOR, NOR, $\mathrm{NO}_{3}^{-}/\mathrm{SO}_{4}^{2-}$ mass ratio and temperature.
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Fig. 3. The diurnal profiles of $50_{4}^{2-}$ , $\mathrm{NO}_{3}^{-}$ , $\mathrm{NH_{4}^{+}}$ and $\mathrm{NO}_{2}^{-}$ in four seasons (a) Spring; (b) Summer; (c) Autumn; (d) Winter.
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Fig. 4. Mean clusters and the corresponding mean ions concentrations in four seasons.
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Fig. 5. RCF distribution for sulfate in Jinan in four seasons.
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Fig. 6. RCF distribution for nitrate in Jinan in four seasons.
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Table 3 Factor loadings from PCA in the four seasons.
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Semi-continuous measurement of water-soluble ions in $\mathsf{P M}_{2.5}$ in Jinan, China: Temporal variations and source apportionments Xiaomei Gao a, Lingxiao Yang a,b,\*, Shuhui Cheng a, Rui Gao a, Yang Zhou a, Likun Xue a, Youping Shou a, Jing Wang a, Xinfeng Wang a, Wei Nie a, Pengju Xu a, Wenxing Wang a,c a Envir...
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10.1016/j.partic.2012.08.004
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Fig. 1. Location of the sampling site in Zhengzhou, Henan, China.
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Table 1 $\mathrm{PM}_{2.5}$ concentration and its chemical composition, collected in 2010.
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Fig. 2. Variation of $\mathrm{PM}_{2.5}$ mass concentration during the sampling period in Zhengzhou, China.
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Fig. 3. Seasonal variations of concentrations $\langle\mathrm{in}\,\upmu\mathrm{g}/\mathrm{m}^{3}\rangle$ ) of $\mathrm{PM}_{2.5}$ (a), total elements (b), TC (c), and total soluble ions (d), with error bars representing standard deviation.
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Fig. 4. Seasonal variations of contributions of total components (a), total elements (b), TC (c) and total ions (d) to $\mathrm{PM}_{2.5}\,(\%)$ , with error bars representing standard deviation.
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Fig. 5. Seasonal variations of (a) water-soluble ions, EC, OC and (b) elements in $\mathrm{PM}_{2.5}$ (in $\upmu\mathrm{g}/\mathfrak{m}^{3}$ , with error bars representing standard deviations.
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Table 2 Average concentrations of $\mathrm{PM}_{2.5}$ , OC and EC in Zhengzhou, Shenzhen, Beijing, Guangzhou and Shanghai, all using NIOSH TOT method.
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Fig. 6. Proflies of factors (contributions of species, $\%$ ). Factor 1: industrial; Factor 2: soil dust; Factor 3: secondary aerosol; Factor 4: biomass burning plus incineration; Factor 5: vehicle and Factor 6: coal combustion.
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Table3 PMF source contribution estimates (SCE) $(\upmu\mathrm{g}/\uppi^{3}$ and percentage) for $\mathrm{PM}_{2.5}$ .
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$\mathsf{P M}_{2.5}$ in an industrial district of Zhengzhou, China: Chemical composition and source apportionment Ningbo Geng a, Jia Wang a, Yifei Xu a, Wending Zhang a, Chun Chen b, Ruiqin Zhang a,∗ a Research Institute of Environmental Science, Department of Chemistry, Zhengzhou University, Zhengzhou 450001, Chin...
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Fig. 1. Location of the sampling site in Tianjin, China.
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Table 1 Statistical summary of the measured species concentrations of $\mathrm{PM}_{2.5}$ in Tianjin.
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Table 2 Comparison of TC, OC, and EC in Tianjin with those in other cities of China.
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Fig. 2. Relationship between OC and EC of $\mathrm{PM}_{2.5}$ in Tianjin.
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Table 3Comparison of element concentrations in major cities of China (ng/m3).
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Fig. 3. Denary logarithm of element enrichment factors of $\mathrm{PM}_{2.5}$ in Tianjin.
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Fig. 4. Average mass composition of 24-h $\mathrm{PM}_{2.5}$ samples in Tianjin.
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Table 4 Pearson’s correlation coefficient matrix of elements in $\mathrm{PM}_{2.5}$
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Chemical composition of $\mathsf{P M}_{2.5}$ during winter in Tianjin, China Jinxia Gu a,b, Zhipeng Bai b,∗, Weifang Li b, Liping Wu a,b, Aixia Liu c, Haiyan Dong d, Yiyang Xie c a Tianjin Institute of Urban Construction, Tianjin 300384, China b State Environmental Protection Key Laboratory of Urban Ambient Air Part...
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Fig 1 The sampling locations in Jinan, China
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Table 1 Summary statistics of $\operatorname{PM}_{2.5}$ , OC, and EC mass concentration at two sampling sites in Jinan, China
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Fig. 2 The distribution of $\operatorname{PM}_{2.5}$ mass concentration at two sampling sites in Jinan, China
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Table 2 Average mass concentrations of inorganic elements at two sampling sites in Jinan, China
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Fig. 3 The seasonal average concentrations of major watersoluble ions for $\operatorname{PM}_{2.5}$ in Jinan, China (in micrograms per cubic meter). a Spring. b Summer. c Fall. d Winter
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Fig. 4 The element enrichment factors of inorganic elements for $\operatorname{PM}_{2.5}$ in Jinan, China
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Fig. 5 The contribution percentages of the chemical species for $\operatorname{PM}_{2.5}$ in Jinan, China. a At EMS. b At SJU
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Fig. 6 PMF source profiles of secondary sulfate, soil dust, secondary nitrate and vehicle emissions, coal combustion, and biomass burning and in Jinan, China. a At EMS. b At SJU
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Fig. 6 (continued) Mass closure analysis
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Major chemical compositions, possible sources, and mass closure analysis of $\mathbf{PM}_{2.5}$ in Jinan, China Jinxia Gu $\cdot$ Shiyong Du $\cdot$ Daowen Han $\cdot$ Lujian Hou $\cdot$ Jing Yi $\cdot$ Ja Xu $\cdot$ Guanghui Liu $\cdot$ Bin Han & Guangwu Yang $\cdot$ Zhi-Peng Bai Received: 6 August 2013 /Accepted: 30 ...
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Table 1 Sampling sites in the Pearl River Delta
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Fig. 1. Location of sites used for the PRD study.
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Table 2 Mean fine particulate matter concentrations across the PRD
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Table 3 Sampling days categorized into southerly, northerly or mixed flow
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Fig. 2. Measurements of wind speed, wind direction and precipitation at the Shenzhen meteorology site for selected days categorized into northerly, southerly and mixed flow. Hourly wind magnitude and direction are indicated by the arrows and $24\,\mathrm{{h}}$ rainfall is indicated by the square markers.
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Fig. 3. Normalized concentrations and standard error of species measured at seven sites in the Pearl River Delta, categorized by wind pattern. Site labels are as follows: Tap Mun (TM), Tung Chung (TC), central/western (CW), Shenzhen (SZ), Zhongshan (ZS). Guangzhou (GZ) and Conghua (CH).
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Table 4 Average concentrations of measured species during southerly, northerly and mixed flow
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Source areas and chemical composition of fine particulate matter in the Pearl River Delta region of China G.S.W. Haglera, , M.H. Bergina,b, L.G. Salmonc, J.Z. Yud, E.C.H. Wand, M. Zhengb, L.M. Zenge, C.S. Kiange, Y.H. Zhange, A.K.H. Lauf, J.J. Schauerg aSchool of Civil and Environmental Engineering, Georgia Institute o...
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10.5194/acp-12-1377-2012
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Table 1. Annual mass concentrations of $\mathrm{PM}_{2.5}$ , its major ionic species and their equivalent ratios at Beijing and Chongqing from March 2005 to February 2006.
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Table 2. Seasonal $\mathrm{R_{C/A}}$ at multiple sites in the northern and southern China $(\mathrm{R}_{\mathrm{C/A}}\!=\!(\mathrm{NH}_{4}^{+}+\mathrm{Ca}^{2+})/(\mathrm{SO}_{4}^{2-}+\mathrm{NO}_{3}^{-})$ (µeq/µeq), spring $=$ MAM, summer $=\mathrm{JJA}$ , $\mathrm{{fall}=S O N}$ , winter $=\mathrm{D}\mathbf{F}_{\mathr...
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Fig. 1. Seasonal variations of (a) $\mathrm{R_{C/A}}$ (uncertainty $=$ standard deviation) and (b) normalized $\mathrm{R_{C/A}}$ of $\mathrm{PM}_{2.5}$ at Beijing and Chongqing.
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Fig. 2. Seasonal variations of in situ $\operatorname{PM}_{2.5}\operatorname{pH}$ , $[\mathrm{H^{+}}]_{\mathrm{Ins}}$ , $[\mathrm{H}_{2}\mathrm{O}]$ and RH at (a) Beijing and (b) Chongqing.
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Fig. 3. (a) Daily variation in the subtropical high over the northwestern Pacific between $110{-}130^{\circ}\,\mathrm{E}$ (Lu et al., 2007); (b) the geopotential heights of $500\,\mathrm{hPa}$ at UTC 08:00, 23 June 2005 over East Asia; (b) the geopotential heights of $500\,\mathrm{hPa}$ at UTC 12:00, 29 June 2005 over ...
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Fig. 4. Clusters of air mass backward trajectories arriving at $500\,\mathrm{m}$ above ground level at Beijing for (a) 4 March–6 May, (c) $7{-}31\ \mathrm{May},$ (d) 1–27 June in 2005 and (b) 3 March–5 May in 2006.
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Fig. 5. Clusters of air mass backward trajectories arriving at $500\,\mathrm{m}$ above ground level at Chongqing for (a) 4 March–6 May, (c) 7–31 May, (d) 1–27 June in 2005 and (b) 3 March–5 May in 2006.
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Fig. 6. Differences of $\mathrm{R_{C/A}}$ , $\mathrm{NO}_{3}^{-}/\mathrm{SO}_{4}^{2-}$ , concentrations of associated ionic species and meteorological factors between the two springs of 2005 and 2006 for (a) Beijing (TH and MY) and (b) Chongqing (JB, DDK and BB), which were calculated based on $(2006{-}2005)/2005$ and ...
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3.5 Formation of $\mathbf{No}_{3}^{-}$ at different levels of aerosol acidity Fig. 7. Monthly variations of (a) amount of precipitation vs. concentration of $\mathrm{NH}_{4}^{+}$ in $\mathrm{PM}_{2.5}$ and (b) equivalent charge ratios of $\mathrm{NH}_{4}^{+}/\mathrm{SO}_{4}^{2-}$ in $\mathrm{PM}_{2.5}$ vs. precipitati...
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Fig. 8. Molar ratios of $[\mathrm{NO}_{3}^{-}]/[\mathrm{SO}_{4}^{2-}]$ vs. $[\mathrm{NH}_{4}^{+}]/[\mathrm{SO}_{4}^{2-}]$ at different (a) acidity and (b) water content $\left(\mathrm{[H}_{2}\mathrm{O}\right)\right)$ in $\mathrm{PM}_{2.5}$ at Beijing (TH and MY) and Chongqing (JB, DDK and BB). LA, less acidic. MA, more...
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Fig. 9. Relationships between molar concentrations of $[\mathrm{NO}_{3}^{-}]$ and $[\mathrm{NH}_{4}^{+}]_{\mathrm{Excess}}$ in $\mathrm{PM}_{2.5}$ at Beijing (TH and MY) and Chongqing (JB, DDK and BB).
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Spatial and seasonal variability of $\mathbf{PM}_{2.5}$ acidity at two Chinese megacities: insights into the formation of secondary inorganic aerosols K. $\mathbf{H}\mathbf{e}^{1}$ , Q. Zhao1, Y. Ma1, F. Duan1, F. Yang2, Z. Shi3, and G. Chen4 1State Key Joint Laboratory of Environment Simulation and Pollution Control, ...
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Fig. 1. $\mathrm{PM}_{2.5}$ samples were taken in two southern China cities: Hong Kong (HK) and Xiamen (XM); and two northern China cities: Beijing (BJ) and Xi'an (XA).
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Table 1 Average $\pm$ standard error) mass concentration and chemical composition of $\mathrm{PM}_{2.5}$ collected at four megacities in China during the haze events.
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Fig. 2. (a) Oxidative potentials, IL-6, IFN- $\cdot Y$ and TNF- $\cdot\mathfrak{x}$ production of the haze $\mathrm{PM}_{2.5}$ collected from BJ, XA, XM and HK. (b) Correlation of oxidative potentials of the haze $\mathrm{PM}_{2.5}$ to IL-6, $\mathrm{IFN-}Y$ and TNF- $\cdot a$ . Carbon black (CB) was served as negative...
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Fig. 3. Correlations of DCFH (oxidative potential), IL-6, $\mathrm{IFN-}Y$ and TNF- $\cdot\mathfrak{a}$ to the chemical compositions (sulfate, nitrate, ammonium, OC, EC, amine, urea and levoglucosan). $^{*}\mathtt{p}<0.05$ .
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Chemical composition and bioreactivity of PM2.5 during 2013 haze events in China Kin-Fai $\mathsf{H o}^{\;\mathsf{a},\,\mathsf{b},\;^{*}}$ , Steven Sai Hang Ho b, c, Ru-Jin Huang b, d, e, Hsiao-Chi Chuang f , g, \*\* Jun-Ji Cao b, h, Yongming Han b, Ka-Hei Lui a, Zhi Ning i, Kai-Jen Chuang j, k, Tsun-Jen Cheng l, m, Sh...
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Fig. 1. Location of monitoring sites: Hong Kong Polytechnic University Campus [PolyU]; Kwun Tong (KT); Hok Tsui (HT).
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Table 1 Method detection limit (MDL) of selected species determined
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Table 2 Average concentrations and standard deviations of selected species at PolyU and KT stations
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Table 3 Comparison of concentrations of metal levels at the major cities of China
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Table 4 Summary of correlation coefficients of selected species in $\mathrm{PM}_{2.5}$ at PolyU stations
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Table 5 Summary of correlation coefficients of selected species in $\mathrm{PM}_{2.5}$ at KT stations
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Table 6 Enrichment factors of major elements and heavy metals at three sampling sites
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Table 7 The results of principal component analysis for selected species in $\mathrm{PM}_{2.5}$ at PolyU stations (varimax with Kaiser normalization)
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Table 8 The results of principal component analysis for selected species in $\mathrm{PM}_{2.5}$ at KT stations (varimax with Kaiser normalization)
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Table 9 The Results of APCA estimated concentration for selected species in $\mathrm{PM}_{2.5}$ at PolyU stations
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Table 10 The results of APCA estimated concentration for selected species in $\mathrm{PM}_{2.5}$ at KT stations
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Fig. 2. Estimated source contribution to $\mathrm{PM}_{2.5}$ at PolyU.
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Fig. 3. Estimated source contribution to $\mathrm{PM}_{2.5}$ at KT.
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Dendrogramusing Ward Method RescaledDistanceClusterCombine Fig. 4. Cluster analysis of selected species in $\mathrm{PM}_{2.5}$ of PolyU station.
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Fig. 5. Cluster analysis of selected species in $\mathrm{PM}_{2.5}$ of KT station.
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Source apportionment of $\mathrm{PM}_{2.5}$ in urban area of Hong Kong K.F. Ho a,∗, J.J. Cao b, S.C. Lee a, Chak K. Chan c a Department of Civil & Structural Engineering The Hong Kong Polytechnic University, Hong Kong, China b State Key Laboratory of Loess & Quaternary Geology Institute of Earth Environment Chinese Aca...
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Fig. 1. Locations of field observation sites. $\,^{*}\!\mathbf{N}\!]$ , SZ, SH, SAES, HZ and NB represent Nanjing, Suzhou, Shanghai, Shanghai Academy of Environmental Sciences, Hangzhou and Ningbo.
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Fig. 2. Particle concentration and visibility during the haze episode.
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Fig. 3. Concentrations of gaseous pollutants during the haze episode.
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Y. Hua et al. / Atmospheric Environment xxx (2015) 1e12 Fig. 4. Chemical composition of $\mathrm{PM}_{2.5}$ in the YRD during the haze episode. \*Chemical composition of $\mathrm{PM}_{2.5}$ in SAES was obtained from continuous monitoring and $K^{+}$ concentration is the original data instead of non-soil $K^{+}$ due to...
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Fig. 5. Wind direction and speed during the haze episode. $^{*}({\sf a})$ is the wind direction of the four cities. Directions of arrows represent wind directions. The arrow direction in the legend represent $0^{\circ}$ (north direction). Meanwhile, in the clockwise direction, 90, 180, $270^{\circ}$ represent east, sou...
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Fig. 6. Relative humidity and temperature during the haze episode.