|
|
|
|
|
|
| Study of Emission Characteristics of Particulate Arsenic, Cadmium, Copper and Lead Derived From Burning of Tibetan Incenses by
ICP-OES Method With Microwave Digestion |
| CHEN Ping-yun1, KANG Xiu-tang1, GUO Liang-qia2* |
1. National Quality Supervision and Inspection Center for Incense Products (Fujian), Quanzhou 362100, China
2. Ministry of Education Key Laboratory for Analytical Science of Food Safety and Biology, College of Chemistry, Fuzhou University, Fuzhou 350116, China
|
|
|
|
|
Abstract Large amounts of particulate and volatile matter derived from incense burning have become one of the important sources of indoor air pollution. At present, the research about the effect of burning incense on the local air environment mainly focused on determining particulate matters, volatile gases, and organic pollutants after incense burning. The research about the emission efficiencies and emission factors of heavy metals and the effect of particulate heavy metals on indoor air quality after incense burning has not been reported. To better understand the emission characteristics of heavy metals in Tibetan incense, nine kinds (S1—S9) of line-type Tibetan incense powder samples and their total suspended particulates (TSP) after burning were digested by microwave digestion method with programmed temperature procedure, and four primary kinds of heavy metal elements (i.e. As, Cd, Cu, Pb) in these powder samples. Their TSP were determined by the inductively coupled plasma optical emission spectrometry (ICP-OES) with the wavelength 188.980 nm for As, 214.439 nm for Cd, 327.395 nm for Cu and 220.353 nm for Pb, respectively. The experiment results are listed as follows: (1) The contents of four heavy metals are different in different Tibetan incense samples, and the average content of heavy metal from high to low is in the order of Cu, Pb, As and Cd. The source of Cd and Pb in different Tibetan incense samples is similar, remarkably different from As and Cu. (2) After burning, the amounts of smoke and dust from different Tibetan incense samples are different. The largest and minimum amounts are 94.75 and 38.52 mg·kg-1, respectively. (3) The emission amounts of As, Cd and Pb in the total suspended particles from different Tibetan incense samples depend on their contents in the incense sample. However, the emission amount of Cu is not only dependent on the incense samples but also affected by other factors. (4) The emission efficiency of heavy metals in Tibetan incense samples from high to low is in the order of Cd, As, Pb, and Cu. The emission factor from high to low is in the order of As, Pb, Cd, and Cu. (5) The emission of As and Cd from Tibetan incense samples after burning may adversely affect local atmosphere quality. Among these samples, the highest contents of As and Cd in the TSP of S3, S5 and S9 from the same manufacturer. The manufacturer is suggested to control the source and content of As and Cd stricter to decrease the heavy metal pollution in indoor air and the risk to human health after burning Tibetan incenses.
|
|
Received: 2021-12-08
Accepted: 2022-04-14
|
|
|
|
Corresponding Authors:
GUO Liang-qia
E-mail: lqguo@fzu.edu.cn
|
|
[1] Qu Y, Ji H, Oudray F, et al. Optik, 2021, 241: 166999.
[2] Zhou R, An Q, Pan X W, et al. Environ. Chem. Lett., 2015, 13: 465.
[3] Zhang Z, Tan L, Huss A, et al. Pediatr. Pulm., 2019: 1.
[4] Chen L Y, Ho C. Environ. Health Persp., 2016, 124(9): 1487.
[5] Hwang Y H, Lin Y S, Lin C Y, et al. Environ. Sci. Pollut. Res., 2014, 21: 13480.
[6] Chen K F, Tsai Y P, Lai C H, et al. Environ. Sci. Pollut. Res., 2021, 28(30): 40640.
[7] Hung D Z, Yang K W, Wu C C, et al. Clin. Toxicol., 2021, 59(8): 756.
[8] Wang M, Qin F, Zhang X. Appl. Mech. Mater., 2011, 71-78: 2740.
[9] Goel S G, Somwanshi S, Mankar S, et al. Air Qual Atmos Health, 2021, 14: 1033.
[10] Cohen R, Sexton K G, Yeatts K B. Sci. Total Environ., 2013, 458-460: 176.
[11] See S W, Balasubramanian R. Build. Environ., 2011, 46(5): 1074.
[12] Lui K H, Musa Bandowe B A, Ho S S H, et al. Environ. Pollut., 2016, 213: 524.
[13] Yang T T, Ho S C, Chuang L T, et al. Environ. Pollut.，2017, 220: 1190.
[14] Bootdee S, Chantara S, Prapamontol T. Atmos. Pollut. Res. 2016, 7: 680.
[15] Kuo S C, Tsai Y I. Sci. Total Environ., 2017, 584-585：495.
[16] Kuo S C, Tsai Y I, Sopajaree K. Atmos. Environ., 2016, 138: 125.
[17] Manoukian A, Buiron D, Temime-Roussel B, et al. Environ. Sci. Pollut. Res., 2016, 23: 6300.
[18] Fang G C, Lin S J, Lee J F, et al. Toxicol. Ind. Health, 2009, 25: 93.
[19] Lu H C, Fang G C, Wu Y S. J. Air Waste Manage., 2006, 56: 1033.
|
| [1] |
LIANG Ye-heng1, DENG Ru-ru1, 2*, LIANG Yu-jie1, LIU Yong-ming3, WU Yi4, YUAN Yu-heng5, AI Xian-jun6. Spectral Characteristics of Sediment Reflectance Under the Background of Heavy Metal Polluted Water and Analysis of Its Contribution to
Water-Leaving Reflectance[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 111-117. |
| [2] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
| [3] |
LIU Hong-wei1, FU Liang2*, CHEN Lin3. Analysis of Heavy Metal Elements in Palm Oil Using MP-AES Based on Extraction Induced by Emulsion Breaking[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3111-3116. |
| [4] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
| [5] |
CHENG Fang-beibei1, 2, GAN Ting-ting1, 3*, ZHAO Nan-jing1, 4*, YIN Gao-fang1, WANG Ying1, 3, FAN Meng-xi4. Rapid Detection of Heavy Metal Lead in Water Based on Enrichment by Chlorella Pyrenoidosa Combined With X-Ray Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2500-2506. |
| [6] |
ZHANG Xia1, WANG Wei-hao1, 2*, SUN Wei-chao1, DING Song-tao1, 2, WANG Yi-bo1, 2. Soil Zn Content Inversion by Hyperspectral Remote Sensing Data and Considering Soil Types[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2019-2026. |
| [7] |
TANG Quan1, ZHONG Min-jia2, YIN Peng-kun2, ZHANG Zhi3, CHEN Zhen-ming1, WU Gui-rong3*, LIN Qing-yu4*. Imaging of Elements in Plant Under Heavy Metal Stress Based on Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1485-1488. |
| [8] |
ZHANG Chao1*, SU Xiao-yu1, XIA Tian2, YANG Ke-ming3, FENG Fei-sheng4. Monitoring the Degree of Pollution in Different Varieties of Maize Under Copper and Lead Stress[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1268-1274. |
| [9] |
ZHU Zhao-zhou1, YAN Wen-rui1, 2, ZHANG Zi-jing1, 2. Research of Pollution Characteristics, Ecological and Health Risks of Heavy Metals in PM2.5 From Fireworks by Inductively Coupled
Plasma-Mass Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 644-650. |
| [10] |
TANG Ju1, 2, DAI Zi-yun2*, LI Xin-yu2, SUN Zheng-hai1*. Investigation and Research on the Characteristics of Heavy Metal Pollution in Children’s Sandpits Based on XRF Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3879-3882. |
| [11] |
PAN Han-jiang, GU Tie-xin, LIU Mei, YANG Rong*, ZHAO Kai, GU Xue. Research Progress and Design on Reference Materials of Atmospheric Particulates[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3659-3664. |
| [12] |
LIU Hong-jun1, NIU Teng1, YU Qiang1*, SU Kai2, YANG Lin-zhe1, LIU Wei1, WANG Hui-yuan1. Inversion and Estimation of Heavy Metal Element Content in Peach Forest Soil in Pinggu District of Beijing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3552-3558. |
| [13] |
JUMAHONG Yilizhati1, 2, TAN Xi-juan1, 2*, LIANG Ting1, 2, ZHOU Yi1, 2. Determination of Heavy Metals and Rare Earth Elements in Bottom Ash of Waste Incineration by ICP-MS With High-Pressure Closed
Digestion Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3168-3173. |
| [14] |
WU Bing, YANG Ke-ming*, GAO Wei, LI Yan-ru, HAN Qian-qian, ZHANG Jian-hong. EC-PB Rules for Spectral Discrimination of Copper and Lead Pollution Elements in Corn Leaves[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3256-3262. |
| [15] |
LI Jun-meng1, ZHAI Xue-dong1, YANG Zi-han1, ZHAO Yan-ru1, 2, 3, YU Ke-qiang1, 2, 3*. Microscopic Raman Spectroscopy for Diagnosing Roots in Apple
Rootstock Under Heavy Metal Copper Stress[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2890-2895. |
|
|
|
|
