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Timeline trend profile and seasonal variations in nicotine present in ambient PM10 samples: A four year investigation from Delhi region, India
Atmospheric Environment 98 (2014) 89e97
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Technical note
Timeline trend profile and seasonal variations in nicotine present in ambient PM10 samples: A four year investigation from Delhi region, India Shweta Yadav a, b, d, Ankit Tandon a, c, Arun K. Attri a, * a
School of Environmental Sciences, Jawaharlal Nehru University, New Delhi 110067, India Department of Environmental Sciences, Central University of Jammu, Jammu 180011, India School of Earth and Environmental Sciences, Central University of Himachal Pradesh, Dharamshala 176215, India d Department of Environmental Studies, Siksha-Bhavana, Visva-Bharati, Santiniketan 731235, India b c
h i g h l i g h t s Tobacco smoke emitted nicotine in environment binds onto the PM10 aerosols. Major proportion of total n-alkanes and PAHs, in PM10 arise from non-ETS sources. Linear trend suggests 16% per annum increase in ambient particulate nicotine from 2006 to 2009. Trends agree well with the derived combustible tobacco consumption trend in Delhi. Non-linear trend timeline suggests a sharp increase in the particulate nicotine in Delhi region.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 May 2014 Received in revised form 21 August 2014 Accepted 22 August 2014 Available online 23 August 2014
The detection of nicotine, an organic tracer for Environmental Tobacco Smoke (ETS), in the collected PM10 samples from Delhi region's ambient environment, in a appropriately designed investigation was initiated over four years (2006e2009) to: (1) Comprehend seasonal and inter-annual variations in the nicotine present in PM10; (2) Extract regression based linear trend profile manifested by nicotine in PM10; (3) Determine the non-linear trend timeline from the nicotine data, and compare it with the obtained linear trend; (4) Suggest the possible use of the designed experiment and analysis to have a qualitative appraisal of Tobacco Smoking activity in the sampling region. The PM10 samples were collected in a monthly time-series sequence at a known receptor site. Quantitative estimates of nicotine (ng m3) were made by using a Thermal Desorption Gas Chromatography Mass Spectrometry (TD-GC/MS). The annual average concentrations of nicotine (ng m3) were 516 ± 302 (2008) > 494 ± 301 (2009) > 438 ± 250 (2007) > 325 ± 149 (2006). The estimated linear trend of 5.4 ng m3 month1 corresponded to 16.3% per annum increase in the PM10 associated nicotine. The industrial production of India's tobacco index normalized to Delhi region's consumption, pegged an increase at 10.5% per annum over this period. © 2014 Elsevier Ltd. All rights reserved.
Keywords: PM10 Environmental Tobacco Smoke Nicotine Trend EEMD
1. Introduction Environmental Tobacco Smoke (ETS) has long been recognized detrimental to human health; it is known to cause respiratory and cardiovascular diseases (Robinson and Yu, 2001; Musk and de Klerk, 2003; Schick, 2011; Wang et al., 2012). These concerns have seen the active initiation of many multilevel public campaigns e local and global e to sensitize the public to the * Corresponding author. E-mail addresses: [email protected], [email protected] (A.K. Attri). http://dx.doi.org/10.1016/j.atmosenv.2014.08.058 1352-2310/© 2014 Elsevier Ltd. All rights reserved.
health risks associated with tobacco smoke: both, for active as well as passive smoking (http://www.who.int/tobacco/en/). The acronym “ETS” designates signature for thousands of toxic chemical compounds present in tobacco smoke, which are known to affect human health through multiple pathways (Jenkins et al., 2000; Yeung and Ward, 2003; Talhout et al., 2011). ETS has been classified as a Group A carcinogen under USEPA's carcinogen assessment guidelines (http://www.epa.gov/ smokefre/pubs/strsfs.html# classification). In atmosphere, tobacco smoke contents, primarily, partition into gas phase, and a significant part of the hazardous constituents of ETS, also gets
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adsorbed into the atmospheric aerosol's matrix; once adsorbed the contents persists for a considerable time in the ambient air and act as a source of second hand/third hand smoke (Schick, pez et al., 2012). As a part of ETS con2011; Matt et al., 2011; Lo stituents, nicotine has a tendency to attach as a salt on to the surface of acidic aerosol particles, and can be detected in the collected aerosol samples deposited on the filter matrix (H€ ager and Niessner, 1997). Ambient aerosols, in general, act as a potential transient reservoir for many chemical compounds (organic, Inorganic, elements etc.), including those released from tobacco smoke. Aerosol associated chemical constituents can become airborne through various reported re-suspension mechanisms, even after their removal from ambient environment as surface deposits (Xu et al., 1994; Tandon et al., 2008; Hoh et al., 2012). Chemical characterization of ETS constituents, in addition to the presence of nicotine, represents a diverse mixture of compounds such as n-alkanes, iso-alkanes, anteiso-alkanes, Polycyclic Aromatic Hydrocarbons (PAHs), steranes, and N-containing compounds (Rogge et al., 1994; Kavouras et al., 1999). Nalkanes and PAHs are also emitted from other multiple sources; whereas, iso-alkanes, anteiso-alkanes and N-containing compounds like nicotine, quinoline, and isoquinoline are considered as the targeted source markers for ETS (Rogge et al., 1994; Schauer et al., 2007). Nicotine (3-[(2S)-1-methylpyrrolidin-2-yl] pyridine), the N-containing alkaloid is a established potential biological tracer for ETS in an indoor and outdoor environment. The compound, on its metabolization, can stay in the body for several days as cotinine (Benner et al., 1989; Rogge et al., 1994; Simoneit, 2002; Schauer et al., 2007). Both, in particle and vapor phase nicotine is one of the most abundantly resolved N-containing compound present in ETS: On inhalation it transcends the blood brain barrier and bio-membranes, can increase pulse rate, elevates blood pressure, enhances blood sugar mobilization and blood catecholamines (Da Silva et al., 2012). Through respiration, nicotine can also get deposited in the respiratory tract through four reported mechanisms: Direct gas deposition; Evaporative gas deposition; Particle deposition entailing evaporation; and Particle deposition involving diffusion (Pankow, 2001). To assess the multifarious implications and health risks of ETS exposure, it is fundamental to estimate the major constituents of ETS; particularly in regions known to carry high aerosol load in the ambient environment, that too over a long term basis to capture monthly, seasonal annual and inter-annual variations (Tandon et al., 2010; Yadav et al., 2013b). In this context, the recent statistical data unambiguously indicates an alarming increase in the tobacco usage and ETS exposure to a large number of non-users in India (Global Adult Tobacco Survey, 2009e2010). Given the risk coupled with ETS exposure to human health, it was considered expedient to undertake a comprehensive investigation to estimate the major constituents of ETS which could be associated with PM10 aerosols in the ambient environment of highly urbanized capital region of Delhi. The Delhi region has high population density, and inhabits a large number of tobacco users. The experiment was designed to collect PM10 aerosol samples in a time series, at a known receptor site (Singh et al., 1997). The analysis of the collected samples was done by using the established Thermal Desorption Gas Chromatography Mass Spectrometry (TD-GCeMS) method (Yadav et al., 2013b). The quantification of the organic tracers such as n-alkanes, PAHs and particulate nicotine was done to assess for the footprints of ETS contributions present in the collected ambient aerosol samples; identification was also done for the presence of other markers (iso-alkanes, anteiso-alkanes and isoquinoline). The sampling carried over four years allowed the analysis for the extraction of multi-scale temporal variability present in the determined concentrations of PM10 bound nicotine by using Additive
Time-series Decomposition (Khalil and Rasmussen, 1990; Tandon and Attri, 2011), and Ensemble Empirical Mode Decomposition (EEMD) algorithm (Huang et al., 1998; Tandon et al., 2013; Wu et al., 2011). The estimated linear and non-linear trends were extracted from the data to characterize the timeline profile of aerosol bound nicotine footprints present in the aerosol samples collected from this region over four years (2006e2009); to the best of our knowledge a first such study of its kind from this region. 2. Methodology 2.1. Description of the sampling site The capital region of Delhi is geographically located between 28 250 N and 28 530 N; 76 500 E and 77 220 E at 216 m above mean sea level in the northern part of India. It has a semi-arid climate influenced by the Himalayan ranges to the north, the Thar Desert to the west, the central hot plains to the south and hilly region to the northeast. Delhi is one of the most polluted cities in the world and is distressed with an unusually high aerosol load in the lower atmosphere (Tandon et al., 2008, 2010). Large amounts of wind-blown dust envelops the city during summer, and the onset of the winter season is marked with a ground-based temperature inversion, which further amplifies the load by lowering the planetary boundary layer (PBL). The sampling was done at Jawaharlal Nehru University (JNU), a known receptor site (Singh et al., 1997), located in the south Delhi ridge area, approximately 100 m above the surrounding area (Fig. 1). All samples were collected on the rooftop of a building at 15 m height. 2.2. Aerosol sample collection The samples of PM10 (aerodynamic diameter 10 mm) were collected every week, for 24 h duration, starting from January 2006 to December 2009 on a pre-baked quartz micro-fiber filter matrix using a respirable dust sampler (Envirotech, model e APM 460 BL). Corresponding field blanks were also collected by keeping the blank filters in the sampler, for the same (24 hourly) duration, exposed to the similar environmental conditions (Huang et al., 1997). Prior to their weighing, all filter papers were stored under controlled temperature and relative humidity conditions. Post sampling, filters were stored in a clean A4 size paper envelopes, covered with aluminum foil to inhibit their exposure to the light and were refrigerated until their analysis. All filters were weighed before and after the sampling on a well calibrated microbalance having a precision to weigh 0.01 mg. 2.3. The analysis of aerosol samples using TD-GCeMS For the identification and quantification of the aerosol associated ETS constituents, TD-20 Thermal desorption system coupled with the gas chromatograph mass spectrometer model GCMSQP2010 Plus (Shimadzu, Japan) was used. The carrier gas (Helium e 99.999% purity) flow rate was auto-adjusted to direct thermally desorbed organic compounds to GCeMS. A standardized optimum program (57 min/TD sample) was used for the analysis of all samples and field blanks. Longitudinally cut sections of the uniform discs were inserted into the TD tubes (SS PE ATD Tube 25049; Supelco, Bellefonte, PA, USA.). Each sample was surrounded with a pre-baked, Silane treated glass wool plugs present on the both sides. Prior to their use in a experiments, the empty TD tubes were baked overnight and purged with a high purity N2 gas. All samples were prepared in a clean room conforming to the dust free conditions. The GC was used under a split-less injection mode; the oven temperature was set initially at 80 C with an isothermal hold time
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Fig. 1. Sampling location, a established receptor site, where the PM10 load in time series sequence was collected over four years (2006e2009).
of 1 min. Stepwise programmed linear temperature ramping included: (1) 5 C min1 to 110 C (3 min hold time), and (2) 5 C min1 further to 320 C (5 min final isothermal hold time). Separation of the desorbed organic compounds was done by using Rxi-5Sil MS fused silica capillary column (Restek, Bellefonte, PA, USA) of 30 m length, 0.25 mm id, and 0.25 mm df. Mass range, m/z 40e900, was scanned at 0.5 s/scan, where ions were produced in a electron impact ionization (EI) mode at 70 eV and they were separated by a high performance quadrupole mass filter. Ion Source temperature and interface temperature was kept at 250 C and 290 C respectively.
fragmentation pattern match from an inbuilt NIST and Wiley mass spectral libraries. Nicotine analytical standard was procured from SigmaeAldrich (Bornem, Belgium). Prior to its loading into TD tubes, the reference standard was spotted on a blank filter discs in five different volumes, representing an increasing concentration of the compound. The details of the steps followed for the identification and quantification of other organic species like n-alkanes and PAHs, using appropriate standards with a complete methodology have been discussed elsewhere (Yadav et al., 2013b). 3. Results and discussions
2.4. Quality assurance and quality control (QA/QC) The recovery experiments were performed to optimize the protocol for the detection and maximum desorption of the organic compounds; 98%e100% recovery of samples and standards through thermal desorption was ascertained. Reproducibility of the standard mix runs and the selected samples was also established by processing the same mix in quintuplicate; analytical precision of better than 5% was obtained. Other relevant details regarding QA/ QC are given elsewhere (Yadav et al., 2013b). 2.5. Identification and quantitative estimation of organic species The identification of the aerosol associated nicotine included: (a) Detection of the base peak at m/z 84 and the molecular ion peak at 162 Da; (b) Occurrence of the distinctive peak cluster fragmentation pattern with qualifying peaks at 133, 119, 42; (c) Comparison of the retention time (RT) with one obtained from the calibration standards (RT at 14.53 min); and (d) Similarity search for the
In this four year investigation, more than 50 aerosol associated non-polar organic compounds (NPOCs) were analyzed to identify and assess the dominant aerosol sources in Delhi region. The NPOCs like n-alkanes, PAHs and isoprenoid hydrocarbons present in the aerosol samples, have already been discussed in detail (Yadav et al., 2013b), where the established diagnostic parameters and the Molecular Diagnostic Ratios (MDRs) were used to find out the contributions arising from the biogenic, petrogenic and pyrogenic activities. These investigations revealed an important information about the involved active sources in the region, but the alarming concentrations of nicotine present in the aerosol samples, accompanied with other cigarette smoke markers like quinoline, isoquinoline and anteiso-alkanes, was important to take note of. This stirred us to evaluate the aerosol associated ETS contributions in a focused manner in Delhi's ambient environment. Wartman et al. (1959) had described cigarette smoke as a dense aerosol, and reported predominant presence of the important organic tracer nicotine in the aerosol samples. The use of cigarette smoke specific
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organic markers like nicotine, solanesol, n-alkanes, iso-alkanes, anteiso-alkanes, PAHs, Dicarboxylic acids, quinoline, isoquinoline, also accompany nicotine as a abundantly resolved organic compound among the outdoor cigarette smoke markers (Rogge et al., 1994).
monthly average nicotine concentrations with the Dilution Correction Factor (DCF), where the respective month's DCF value was calculated as (Yadav et al., 2013a, 2013b):
3.1. Profile of ETS associated nicotine
The DCF scaled monthly average nicotine mass concentrations indicate the actual influx or efflux of aerosol associated nicotine, independent of PBL variation (or independent of the changes in the mixing volume). The monthly means of the scaled nicotine concentrations (ng m3) manifested a different temporal profile (Fig. 2, Panel B): higher emissions during April (2007), MarcheAprileMay (2008), and AprileMayeJune (2009) were noted; however, no such pattern was evident for 2006. The mass fraction (ppm) of nicotine in respective samples, with respect to PM10 load (mg m3) is shown in panel C of Fig. 2. Impact of seasonal variations in the temporal plot of nicotine (ppm) is evident. The sample corresponding to November, 2009 requires a special mention as this sample was collected during an episodic event: On 29th October, 2009, there was a fire outbreak in a Indian Oil Corporation Depot located in Sitapura Industrial Area, Jaipur, Rajasthan (located approximately 265 km south west of Delhi). The blaze continued till 11th November, 2009 and the immense fossil fuel burning vitiated the air quality of Delhi region (Singh et al., 2010). In November, 2009, the unusually high nicotine mass concentration, and scaled nicotine concentration was noticed (Panel A and Panel B of Fig. 2). It is reasonable to infer that this peak does not arise due to an excessive smoking on that particular day; it may be due to the chemical reaction between nicotine and other fossil fuel burning markers, or the transported aged nicotine associated with the aerosol particles into Delhi region's environment. However, the mass fraction of nicotine (ppm) in Panel C does not reflect a very prominent peak, implying that the nicotine mass fraction was quite less in comparison to other aerosol associated organic and inorganic compounds (Yadav et al., 2013b). Apart from nicotine, ETS contains complex entity of compounds like pyridines, indoles, quinolines, aromatic hydrocarbons etc. (Rogge et al., 1994; Schauer et al., 2007); the presence of quinoline and isoquinoline compounds in most of the analyzed aerosol samples in the present study validates the above stated conclusion that the traces of cigarette smoke contributes significantly to Delhi's ambient environment.
Nicotine, a main constituent of ETS, is present in both the gas and particle phase (Lofroth, 1995; Pankow, 2001). Investigations reveal that nicotine in a fresh ETS is found in a gaseous phase, however as the ETS ages with time, the gas phase nicotine gets associated with the ambient aerosols (Caka et al., 1990; Lofroth, 1995). The mass concentrations of the aerosol associated nicotine determined in the present investigations are shown in Table 1. The table also compares these values with that reported in earlier studies, though in a different set up and conditions. Nicotine, noticeably, registered a highest individual concentration for all other organic compounds analyzed in this study; the mean nicotine concentration was 448 ± 260 ng m3 (average of the monthly mean concentrations over four years). The time dependent variations in the mass concentrations of nicotine (ng m3), scaled nicotine concentrations (ng m3) and the mass fractions of nicotine (ppm) over four year period are shown in Fig. 2(AeC). The scaled nicotine concentrations takes into account the variations in the mixing volume due to the variation in the planetary boundary layer (PBL) height as explained later in this section. Similarly, the mass fraction concentrations are normalized with respect to the aerosol load of the sample to make it independent of the variations in the aerosol load. The monthly profiles plotted in Panel A of Fig. 2 suggests that the mass concentration of nicotine peaks in the winter months e October, December and November for the year 2006, 2008 and 2009 respectively. The month of April in year 2007, encountered the maximum nicotine concentration, whereas in the same month a minimum concentration was observed in the year 2006. It can be inferred that the monthly mean concentrations of nicotine are found to be governed by the variations in source emissions. Mean mass concentrations of nicotine (ng m3) were 516 ± 302 (2008) > 494 ± 301 (2009) > 438 ± 250 (2007) > 325 ± 149 (2006), suggesting a substantial increase in the tobacco smoking activity over four years. The mass concentration of aerosol associated nicotine (ng m3), in an individual sample, would depend on the mixing volume of the air in which it is present. The mixing volume of air consequently would impact the mass concentration of aerosol, and it follows that the measured concentration of the associated nicotine would need normalization with respect to the variations in the mixing volume; i.e. variations in the PBL. Consequently, to account for this variation we calculated scaled nicotine concentrations by multiplying the
Table 1 Comparison of Nicotine concentration with earlier reported data. S. No.
Experimental setup/source
Nicotine concentration
Reference
1. 2.
PM10 associated nicotine Cigarette smoke collection (1 m3/min) by High volume sampler Ambient nicotine level in common area Cigarette smoke Household dust
448 ± 260 ng m3 467 ± 144 (mmol/g)
Present study Benner et al. (1989)
12.6e60.3 mg m2
Thompson et al. (1989) Rogge et al. (1994) Whitehead et al. (2009) Hoh et al. (2012)
3. 4. 5. 6.
Settled house dust in Smoker homes
1159 mg/cigarette Upto 35,000 ng/g 58.8 mg m2
DCFmonth ¼
Monthly average PBL Annual average PBL
3.2. n-alkanes associated with ETS Considerably high emission rates (549 mg/cigarette) of total nalkanes present in cigarette smoke related aerosol emissions is known (Rogge et al., 1994). It is also established that the cigarette smoke related aerosols display a unique profile of the present nalkanes, where a dominance of odd numbered congeners (C27, C29 and C31), and the most abundant n-alkane (Cmax) at C31 was evident. A similar profile was observed when the contributions to the ambient aerosols also include material arising from leaf abrasion, road dust, garden soil etc.; the presence of n-alkanes in ETS is believed to be from the tobacco leaf surface wax (Rogge et al., 1994; Schauer et al., 2007). It is pertinent to mention here that the homologous series of n-alkanes (C11eC35) investigated in this study does reflect the reported profile: an overall dominance of C27, C29 and C31 congeners was noticed in the ambient aerosol samples with Cmax corresponding with C29 and C31 over this study period. But, a weak correlation (0.125) between homologous series of total n-alkanes and nicotine suggests that the predominant n-alkanes present in the aerosols arose from sources other than the tobacco smoke; only a small contribution may come from cigarette smoke
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Fig. 2. Plotted monthly mean values of nicotine concentration (ng m3); scaled values of nicotine (ng m3) to PBL modulations; and mass fractions of nicotine (ppm) are shown in panel [A], [B] and [C] respectively.
in Delhi region's ambient environment (Fig. 3). The notable presence of isoalkanes and anteisoalkanes in the aerosol samples further supports the argument that the cigarette smoke is also an important source of aerosol associated n-alkanes in Delhi region; similar to the findings reported from Los Angeles (Hildemann et al., 1991).
3.3. PAHs associated with ETS In addition to nicotine's presence in the aerosol samples, the presence of PAHs was significant among the non-polar organic compounds (Yadav et al., 2013b). The concentration of particulate PAHs over four year period was 373 ± 197 ng m3 (2006); 287 ± 98 ng m3 (2007); 410 ± 206 ng m3 (2008); 536 ± 260 ng m3 (2009). The presence of 4-ring PAHs (Fluoranthene and Pyrene) and 6 ring PAHs [Benzo (ghi) perylene] indicated their contributions arising from aerosol associated ETS activity (Lu and Zhu, 2007; Slezakova et al., 2009). Tobacco burning emits all 16 criteria PAHs, but only the concentrations of specific PAHs like chrysene, fluoranthene, benzo(a)anthracene, anthracene,
pyrene, and phenanthrene are reported to be significantly high in ETS (Shihadeh and Saleh, 2005; Ding et al., 2006, 2007; Moir et al., 2008; Hoh et al., 2012). PAHs can also be emitted by other natural and anthropogenic sources involving incomplete combustion, therefore to determine the exact amount of PAHs originating from only cigarette smoke is complicated. A weak correlation between total PAHs and nicotine (0.174) implied that only a small contributions of PAHs are part of ETS (Fig. 4) and their dominant contribution and presence in the aerosols comes from other sources (vehicular exhaust, biomass burning) in Delhi. A weak correlation (0.105) between nicotine and PAHs concentrations is also reported earlier (Hoh et al., 2012).
3.4. Temporal variability in nicotine concentration over four years (2006e2009) The presence of a temporal variability over four years is evident from the plotted monthly average concentrations of nicotine (Fig. 2(AeC)): (i) Concentration in ng m3; (ii) DCF based scalednicotine values; and (iii) the mass fraction of nicotine
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(ng m3). In this analysis, we assume that the monthly mean concentrations can be represented as a linear combination of seasonal variability, trend and random noise; the trend itself is a combination of linear trend and inter-annual variability e the essence is captured by following two equations.
Monthly mean concðtÞ ¼ Seasonal variationðtÞ þ TrendðtÞ þ Random noiseðtÞ (1) TrendðtÞ ¼ Linear trendðtÞ þ Inter-annual variationðtÞ
Fig. 3. Calculated linear correlation between total n-alkane (ng m3) and nicotine (ng m3) mass concentrations present in PM10 load over four year time span (2006e2009).
respectively. The observed variability may be induced by multiple natural factors and temporal variations associated in the involved sources activity. It was worthwhile to extract and assign meaning to the presence of multi-scale variability in the time series of particulate associated nicotine concentrations in terms of underlying physical processes (seasonal or inter-annual influences) Additive Time-series Decomposition (Khalil and Rasmussen, 1990; Tandon and Attri, 2011) and Ensemble Empirical Mode Decomposition (EEMD) based analysis (Huang et al., 1998; Wu et al., 2011; Tandon et al., 2013) was done on the stated time-series data of the nicotine. The methods, besides extracting the embedded variability in the nicotine, also determined the trend present in the particulate nicotine samples collected over the study period (2006e2009). 3.4.1. Determination of seasonal variability and trends in aerosol bound nicotine using additive time series decomposition analysis Additive time-series decomposition analysis was performed on the monthly mean concentration of aerosol bound nicotine
Fig. 4. Calculated linear correlation between total PAHs (ng m3) and nicotine (ng m3) mass concentrations present in PM10 load over four year time span (2006e2009).
(2)
Detailed methodology of the additive time-series decomposition analysis is discussed elsewhere in detail (Tandon and Attri, 2011; Tandon et al., 2012). In Fig. 5A, contributions, with associated standard deviations in the estimated seasonal cyclic variations in the monthly mean concentration of nicotine are plotted as anomalies. The significant standard deviation is associated with the seasonal cyclic variations plotted as the mass concentration of nicotine (ng m3) from Jan 2006 to Dec 2009. Positive nicotine contributions are registered for the post monsoon and winter months (October to January), and negative contributions occur during the monsoon (June to September). During winter months, an expected decrease in the height of the planetary boundary layer would enhance the concentration of both PM10 and associated nicotine/volume. Whereas, during the monsoon, both PM10 and associated nicotine washout is expected, as nicotine has a tendency to attach as a salt on to the €ger and Niessner, 1997). Positive conacidic aerosol particles (Ha tributions with associated large standard deviation noted during April can be attributed to high nicotine bound PM10 load sourced from the dust-storm assisted re-suspension of the surface deposited aerosols (Tandon et al., 2008). In Fig. 5B, a linear trend (linear regression fit) estimate for; a) original monthly mean concentration of PM10 associated nicotine, and b) de-seasonalized monthly mean concentration of PM10 associated nicotine are plotted. Almost a perfect overlap of the linear trend lines is observed for the deseasonalized monthly mean time-series data-set and that for the original data-set. The error estimates with the calculated trend at 95% confidence for the deseasonalized monthly mean time-series data-set was significantly less than the error estimates obtained for the trend line fitted to the original data (Fig. 5B). It is interesting to note that the mass concentration of nicotine present in the ambient aerosol samples increased at the rate of 5.4 ng m3 month1; an increase of ~16.3%/ year. This noticeably implied that tobacco smoking activity in Delhi region has increased between 2006 and 2009.
3.4.2. Extraction of variability and non-linear trend using EEMD One of the limitations of determining the embedded trend is linked with the presence of temporal variability (modulations), non-linearity and departure from the stationarity in the time series data set, like four year nicotine data used in the present analysis (Tandon and Attri, 2011). EEMD analysis circumvents these limitations by allowing the stepwise extraction of multi-scale Internal Mode Functions (IMFs), reflecting cyclic variability present in the data; each IMF differs in terms of period (frequency) and magnitude. Subsequent to the extraction of all IMFs the residuals correspond to the trend present in the data set. Both, the extraction of IMFs and the determination of the embedded trend are tested for their statistical significance. The sensitivity analysis of IMFs and estimated trend is also determined by adding different magnitude of random white noise to the data-set before its EEMD analysis. The details of the underlying philosophy, and the steps required are
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Fig. 5. Calculated monthly variability plotted as histograms from the nicotine time series in panel (A). In panel (B) the calculated linear trend (black line) is plotted using a deseasonalized time series of nicotine (black points).
Fig. 6. Statistically significant ensemble average IMF2 having 6 month period is plotted in panel [A] of the Figure (thick black line); the surrounding redlines show the sensitivity of the estimated IMF2 timeline at 95% confidence. Panel [B] plots the ensemble average trend line (thick black line) drawn through the nicotine monthly average values (ng m3) over four year time period (2006e2009). The surrounding redlines represents the sensitivity of determined trend at 95% confidence.
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discussed in detail elsewhere (Huang et al., 1998; Wu and Huang, 2009; Wu et al., 2011; Chen et al., 2013; Kuo et al., 2013; Tandon et al., 2013; Wu et al., 2011). EEMD analysis of the nicotine timeseries data extracted four IMFs, only IMF2 was found to be statistically significant as per the established criteria (Wu and Huang, 2009; Wu et al., 2011). The noise assisted analysis of 5 different sets of nicotine time-series (Wu and Huang, 2009), enabled the sensitivity analysis of each IMF, and that of calculated embedded trend (Fig. 6A and B). The period of IMF2 (Internal mode function having second highest frequency and representing cyclic variation in the data) determined using Fourier analysis was 6 month (Fig. 6A). A close scrutiny of the considered meteorological variables suggested that similar variation also occurs in PBL (planetary boundary layer) over 6 month which is reflected in the determined IMF2 (i.e. statistically significant Internal Mode Function 2). This cyclic variability in PBL may introduce to a large extent the 6 monthly variability observed in the timeline trend profile of nicotine (Fig. 2A). It is reasonable to infer that the earlier stated rationale to normalize the experimentally determined nicotine profile (Fig. 2A) by using DCF, which accounts for the PBL induced changes in the mixing volume is necessary. The average embedded trend and the sensitivity range determined using EEMD is shown in Fig. 6B, the trend is plotted along with the trend profile of nicotine. The trend timeline over four years is non-linear and the rate of change registers a sharp increase with time. Statistically, the determined trend sensitivity spread region is significantly lower than that observed for the regression based linear trend obtained for de-seasonalized data set of nicotine (Fig. 5B). Unlike linear trend, the non-linear trend provides better appreciation of the change in the timeline profile of nicotine trend associated with the ambient aerosol load over four years. 4. Conclusions Detection of nicotine bound to PM10 load, an important marker compound present in ETS, can reflect qualitative appraisal of tobacco smoking activities. Annual average mass concentrations of nicotine (ng m3) present in the aerosol samples collected over four years were 516 ± 302 (2008) > 494 ± 301 (2009) > 438 ± 250 (2007) > 325 ± 149 (2006). The presence of nicotine and ETS associated other organic compounds (n-alkanes, iso-alkanes, anteiso-alkanes, PAHs, Isoquinoline etc.) helps in assessing the ETS activity and partitioning of associated compounds from gaseous to particle phase (aerosol matrix) in the surrounding environment. The poor correlation shown by nicotine with total nalkanes (0.125) and nicotine with total PAHs (0.174) suggests that most of the aerosol associated total n-alkane and total PAHs in the region does not come from tobacco smoke. The timeline profile of nicotine present in collected aerosol samples, over four years, was subjected to Additive Time-Series Decomposition, and Ensemble Empirical Mode Decomposition to extract the seasonal variability and embedded trend. The estimated linear trend of 5.4 ng m3 month1 corresponds to 16.3% per annum increase in PM10 associated nicotine. EEMD analysis based determined nonlinear trends, too, indicated a steep increase in nicotine present in ambient aerosol samples collected in Delhi region between 2006 and 2009. The survey reports establish that in excess of 30% of the population above the age of 15 years either smoke or chews tobacco (Rani et al., 2003; Ng et al., 2014), and if the increase in the concentration of particulate nicotine, as reported in present study is taken into account (non-linear trend) then it can be inferred that the total number of tobacco users in Delhi region are on the rise. In the absence of any data on tobacco consumption from 2006 to 2009 from Delhi region, to validate the calculated 16% per annum increase in the nicotine associated with PM10, the indirect estimates
were derived. All India figures of the industrial production of tobacco products from 2006 to 2009 were taken to represent the national consumption (Economic Survey, 2011). The state wise estimates during 2006e2009 for Delhi region show that 24% of the total population smoke tobacco products in one form or other, whereas the proportion at India level stands at 35% (GATS survey, 2011). On the assumption that the manufactured tobacco products are all consumed, the Delhi region's consumption can be derived in proportion to the population smoking tobacco products nationally, and in Delhi region between 2006 and 2009. The estimated linear trends from this indirectly derived data for Delhi region was 10.4% per annum; the figure is close to the estimated trend of 16% calculated from the nicotine bound to PM10. The difference between two figures may arise on account of the re-suspension of the surface deposited nicotine associated aerosol load back into the environment's PM10 load having nicotine. Findings from this study pose a question on the societal efforts to eradicate tobacco smoking menace? Measures are required to control the use of tobacco; otherwise the economic burden to tackle the expected increase in the ETS related diseases will be enormous. The investigation presented also emphasize that the detection of nicotine present in ambient particulate load may act as a useful tool to appraise qualitative assessment of ETS activities. Acknowledgments Authors extend their gratitude to the financial support provided in the form of a project by Council for Scientific and Industrial Research, India. SY acknowledges financial assistance provided by the University Grant Commission in the form of Senior Research Fellowship. AT is thankful to the Department of Science and Technology, Government of India for the financial assistance provided to him, in the form of a research project for young scientists. We thank Advanced Instrumentation Research Facility, Jawaharlal Nehru University, for TD-GC-MS facility. References Benner, C.L., Bayona, J.M., Caka, F.M., Tang, H., Lewis, L., Crawford, J., Lamb, J.D., Lee, M.L., Lewis, E.A., Hansen, L.D., Eatough, D.J., 1989. Chemical composition of environmental tobacco smoke. 2. Particuiate-phase compounds. Environ. Sci. Technol. 23, 688e699. Caka, F.M., Eatough, D.J., Lewis, E.A., Tang, H., Hammond, S.K., Leaderer, B.P., Koutrakis, P., Spengler, J.D., Fasano, A., McCarthy, J., Ogden, M.W., Lewtas, J., 1990. An intercomparison of sampling techniques for nicotine in indoor environments. Environ. Sci. Technol. 24, 1196e1203. Chen, X., Zhang, Y., Zhang, M., Feng, Y., Wu, Z., Qiao, F., Huang, N.E., 2013. Intercomparison between observed and simulated variability in global ocean heat content using empirical mode decomposition, part I: modulated annual cycle. Clim. Dyn. 41, 2797e2815. Da Silva, F.R., Da Silva, J., Allgayer, M.Da.C., Simon, C.F., Dias, J.F., dos Santos, C.E.I., Salvador, M., Branco, C., Schneider, N.B., Kahl, V., Rohr, P., Kvitko, K., 2012. Genotoxic biomonitoring of tobacco farmers: biomarkers of exposure, of early biological effects and of susceptibility. J. Hazard. Mater. 225e226, 81e90. Ding, Y.S., Yan, X.J., Jain, R.B., Lopp, E., Tavakoli, A., Polzin, G.M., Stanfill, S.B., Ashley, D.L., Watson, C.H., 2006. Determination of 14 polycyclic aromatic hydrocarbons in mainstream smoke from U.S. brand and non-U.S. brand cigarettes. Environ. Sci. Technol. 40 (4), 1133e1138. Ding, Y.S., Ashley, D.L., Watson, C.H., 2007. Determination of 10 carcinogenic polycyclic aromatic hydrocarbons in mainstream cigarette smoke. J. Agric. Food Chem. 55, 5966e5973. Economic Survey 2010e11, 2011. Statistical Appendix. Government of India. Available at: http://indiabudget.nic.in. Global Adult Tobacco Survey, 2009e2010, 2010. Ministry of Health and Family Welfare. Government of India. Available at: http://mohfw.nic.in/ WriteReadData/l892s/1455618937GATS%20India.pdf. €ger, B., Niessner, R., 1997. On the distribution of nicotine between the gas and Ha particle phase and its measurement. Aerosol Sci. Technol. 26 (2), 163e174. http://dx.doi.org/10.1080/02786829708965422. Hildemann, L.M., Markowski, G.R., Cass, G.R., 1991. Chemical composition of emissions from urban sources of fine organic aerosols. Environ. Sci. Technol. 25, 744e759.
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Technical note
Timeline trend profile and seasonal variations in nicotine present in ambient PM10 samples: A four year investigation from Delhi region, India Shweta Yadav a, b, d, Ankit Tandon a, c, Arun K. Attri a, * a
School of Environmental Sciences, Jawaharlal Nehru University, New Delhi 110067, India Department of Environmental Sciences, Central University of Jammu, Jammu 180011, India School of Earth and Environmental Sciences, Central University of Himachal Pradesh, Dharamshala 176215, India d Department of Environmental Studies, Siksha-Bhavana, Visva-Bharati, Santiniketan 731235, India b c
h i g h l i g h t s Tobacco smoke emitted nicotine in environment binds onto the PM10 aerosols. Major proportion of total n-alkanes and PAHs, in PM10 arise from non-ETS sources. Linear trend suggests 16% per annum increase in ambient particulate nicotine from 2006 to 2009. Trends agree well with the derived combustible tobacco consumption trend in Delhi. Non-linear trend timeline suggests a sharp increase in the particulate nicotine in Delhi region.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 May 2014 Received in revised form 21 August 2014 Accepted 22 August 2014 Available online 23 August 2014
The detection of nicotine, an organic tracer for Environmental Tobacco Smoke (ETS), in the collected PM10 samples from Delhi region's ambient environment, in a appropriately designed investigation was initiated over four years (2006e2009) to: (1) Comprehend seasonal and inter-annual variations in the nicotine present in PM10; (2) Extract regression based linear trend profile manifested by nicotine in PM10; (3) Determine the non-linear trend timeline from the nicotine data, and compare it with the obtained linear trend; (4) Suggest the possible use of the designed experiment and analysis to have a qualitative appraisal of Tobacco Smoking activity in the sampling region. The PM10 samples were collected in a monthly time-series sequence at a known receptor site. Quantitative estimates of nicotine (ng m3) were made by using a Thermal Desorption Gas Chromatography Mass Spectrometry (TD-GC/MS). The annual average concentrations of nicotine (ng m3) were 516 ± 302 (2008) > 494 ± 301 (2009) > 438 ± 250 (2007) > 325 ± 149 (2006). The estimated linear trend of 5.4 ng m3 month1 corresponded to 16.3% per annum increase in the PM10 associated nicotine. The industrial production of India's tobacco index normalized to Delhi region's consumption, pegged an increase at 10.5% per annum over this period. © 2014 Elsevier Ltd. All rights reserved.
Keywords: PM10 Environmental Tobacco Smoke Nicotine Trend EEMD
1. Introduction Environmental Tobacco Smoke (ETS) has long been recognized detrimental to human health; it is known to cause respiratory and cardiovascular diseases (Robinson and Yu, 2001; Musk and de Klerk, 2003; Schick, 2011; Wang et al., 2012). These concerns have seen the active initiation of many multilevel public campaigns e local and global e to sensitize the public to the * Corresponding author. E-mail addresses: [email protected], [email protected] (A.K. Attri). http://dx.doi.org/10.1016/j.atmosenv.2014.08.058 1352-2310/© 2014 Elsevier Ltd. All rights reserved.
health risks associated with tobacco smoke: both, for active as well as passive smoking (http://www.who.int/tobacco/en/). The acronym “ETS” designates signature for thousands of toxic chemical compounds present in tobacco smoke, which are known to affect human health through multiple pathways (Jenkins et al., 2000; Yeung and Ward, 2003; Talhout et al., 2011). ETS has been classified as a Group A carcinogen under USEPA's carcinogen assessment guidelines (http://www.epa.gov/ smokefre/pubs/strsfs.html# classification). In atmosphere, tobacco smoke contents, primarily, partition into gas phase, and a significant part of the hazardous constituents of ETS, also gets
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adsorbed into the atmospheric aerosol's matrix; once adsorbed the contents persists for a considerable time in the ambient air and act as a source of second hand/third hand smoke (Schick, pez et al., 2012). As a part of ETS con2011; Matt et al., 2011; Lo stituents, nicotine has a tendency to attach as a salt on to the surface of acidic aerosol particles, and can be detected in the collected aerosol samples deposited on the filter matrix (H€ ager and Niessner, 1997). Ambient aerosols, in general, act as a potential transient reservoir for many chemical compounds (organic, Inorganic, elements etc.), including those released from tobacco smoke. Aerosol associated chemical constituents can become airborne through various reported re-suspension mechanisms, even after their removal from ambient environment as surface deposits (Xu et al., 1994; Tandon et al., 2008; Hoh et al., 2012). Chemical characterization of ETS constituents, in addition to the presence of nicotine, represents a diverse mixture of compounds such as n-alkanes, iso-alkanes, anteiso-alkanes, Polycyclic Aromatic Hydrocarbons (PAHs), steranes, and N-containing compounds (Rogge et al., 1994; Kavouras et al., 1999). Nalkanes and PAHs are also emitted from other multiple sources; whereas, iso-alkanes, anteiso-alkanes and N-containing compounds like nicotine, quinoline, and isoquinoline are considered as the targeted source markers for ETS (Rogge et al., 1994; Schauer et al., 2007). Nicotine (3-[(2S)-1-methylpyrrolidin-2-yl] pyridine), the N-containing alkaloid is a established potential biological tracer for ETS in an indoor and outdoor environment. The compound, on its metabolization, can stay in the body for several days as cotinine (Benner et al., 1989; Rogge et al., 1994; Simoneit, 2002; Schauer et al., 2007). Both, in particle and vapor phase nicotine is one of the most abundantly resolved N-containing compound present in ETS: On inhalation it transcends the blood brain barrier and bio-membranes, can increase pulse rate, elevates blood pressure, enhances blood sugar mobilization and blood catecholamines (Da Silva et al., 2012). Through respiration, nicotine can also get deposited in the respiratory tract through four reported mechanisms: Direct gas deposition; Evaporative gas deposition; Particle deposition entailing evaporation; and Particle deposition involving diffusion (Pankow, 2001). To assess the multifarious implications and health risks of ETS exposure, it is fundamental to estimate the major constituents of ETS; particularly in regions known to carry high aerosol load in the ambient environment, that too over a long term basis to capture monthly, seasonal annual and inter-annual variations (Tandon et al., 2010; Yadav et al., 2013b). In this context, the recent statistical data unambiguously indicates an alarming increase in the tobacco usage and ETS exposure to a large number of non-users in India (Global Adult Tobacco Survey, 2009e2010). Given the risk coupled with ETS exposure to human health, it was considered expedient to undertake a comprehensive investigation to estimate the major constituents of ETS which could be associated with PM10 aerosols in the ambient environment of highly urbanized capital region of Delhi. The Delhi region has high population density, and inhabits a large number of tobacco users. The experiment was designed to collect PM10 aerosol samples in a time series, at a known receptor site (Singh et al., 1997). The analysis of the collected samples was done by using the established Thermal Desorption Gas Chromatography Mass Spectrometry (TD-GCeMS) method (Yadav et al., 2013b). The quantification of the organic tracers such as n-alkanes, PAHs and particulate nicotine was done to assess for the footprints of ETS contributions present in the collected ambient aerosol samples; identification was also done for the presence of other markers (iso-alkanes, anteiso-alkanes and isoquinoline). The sampling carried over four years allowed the analysis for the extraction of multi-scale temporal variability present in the determined concentrations of PM10 bound nicotine by using Additive
Time-series Decomposition (Khalil and Rasmussen, 1990; Tandon and Attri, 2011), and Ensemble Empirical Mode Decomposition (EEMD) algorithm (Huang et al., 1998; Tandon et al., 2013; Wu et al., 2011). The estimated linear and non-linear trends were extracted from the data to characterize the timeline profile of aerosol bound nicotine footprints present in the aerosol samples collected from this region over four years (2006e2009); to the best of our knowledge a first such study of its kind from this region. 2. Methodology 2.1. Description of the sampling site The capital region of Delhi is geographically located between 28 250 N and 28 530 N; 76 500 E and 77 220 E at 216 m above mean sea level in the northern part of India. It has a semi-arid climate influenced by the Himalayan ranges to the north, the Thar Desert to the west, the central hot plains to the south and hilly region to the northeast. Delhi is one of the most polluted cities in the world and is distressed with an unusually high aerosol load in the lower atmosphere (Tandon et al., 2008, 2010). Large amounts of wind-blown dust envelops the city during summer, and the onset of the winter season is marked with a ground-based temperature inversion, which further amplifies the load by lowering the planetary boundary layer (PBL). The sampling was done at Jawaharlal Nehru University (JNU), a known receptor site (Singh et al., 1997), located in the south Delhi ridge area, approximately 100 m above the surrounding area (Fig. 1). All samples were collected on the rooftop of a building at 15 m height. 2.2. Aerosol sample collection The samples of PM10 (aerodynamic diameter 10 mm) were collected every week, for 24 h duration, starting from January 2006 to December 2009 on a pre-baked quartz micro-fiber filter matrix using a respirable dust sampler (Envirotech, model e APM 460 BL). Corresponding field blanks were also collected by keeping the blank filters in the sampler, for the same (24 hourly) duration, exposed to the similar environmental conditions (Huang et al., 1997). Prior to their weighing, all filter papers were stored under controlled temperature and relative humidity conditions. Post sampling, filters were stored in a clean A4 size paper envelopes, covered with aluminum foil to inhibit their exposure to the light and were refrigerated until their analysis. All filters were weighed before and after the sampling on a well calibrated microbalance having a precision to weigh 0.01 mg. 2.3. The analysis of aerosol samples using TD-GCeMS For the identification and quantification of the aerosol associated ETS constituents, TD-20 Thermal desorption system coupled with the gas chromatograph mass spectrometer model GCMSQP2010 Plus (Shimadzu, Japan) was used. The carrier gas (Helium e 99.999% purity) flow rate was auto-adjusted to direct thermally desorbed organic compounds to GCeMS. A standardized optimum program (57 min/TD sample) was used for the analysis of all samples and field blanks. Longitudinally cut sections of the uniform discs were inserted into the TD tubes (SS PE ATD Tube 25049; Supelco, Bellefonte, PA, USA.). Each sample was surrounded with a pre-baked, Silane treated glass wool plugs present on the both sides. Prior to their use in a experiments, the empty TD tubes were baked overnight and purged with a high purity N2 gas. All samples were prepared in a clean room conforming to the dust free conditions. The GC was used under a split-less injection mode; the oven temperature was set initially at 80 C with an isothermal hold time
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Fig. 1. Sampling location, a established receptor site, where the PM10 load in time series sequence was collected over four years (2006e2009).
of 1 min. Stepwise programmed linear temperature ramping included: (1) 5 C min1 to 110 C (3 min hold time), and (2) 5 C min1 further to 320 C (5 min final isothermal hold time). Separation of the desorbed organic compounds was done by using Rxi-5Sil MS fused silica capillary column (Restek, Bellefonte, PA, USA) of 30 m length, 0.25 mm id, and 0.25 mm df. Mass range, m/z 40e900, was scanned at 0.5 s/scan, where ions were produced in a electron impact ionization (EI) mode at 70 eV and they were separated by a high performance quadrupole mass filter. Ion Source temperature and interface temperature was kept at 250 C and 290 C respectively.
fragmentation pattern match from an inbuilt NIST and Wiley mass spectral libraries. Nicotine analytical standard was procured from SigmaeAldrich (Bornem, Belgium). Prior to its loading into TD tubes, the reference standard was spotted on a blank filter discs in five different volumes, representing an increasing concentration of the compound. The details of the steps followed for the identification and quantification of other organic species like n-alkanes and PAHs, using appropriate standards with a complete methodology have been discussed elsewhere (Yadav et al., 2013b). 3. Results and discussions
2.4. Quality assurance and quality control (QA/QC) The recovery experiments were performed to optimize the protocol for the detection and maximum desorption of the organic compounds; 98%e100% recovery of samples and standards through thermal desorption was ascertained. Reproducibility of the standard mix runs and the selected samples was also established by processing the same mix in quintuplicate; analytical precision of better than 5% was obtained. Other relevant details regarding QA/ QC are given elsewhere (Yadav et al., 2013b). 2.5. Identification and quantitative estimation of organic species The identification of the aerosol associated nicotine included: (a) Detection of the base peak at m/z 84 and the molecular ion peak at 162 Da; (b) Occurrence of the distinctive peak cluster fragmentation pattern with qualifying peaks at 133, 119, 42; (c) Comparison of the retention time (RT) with one obtained from the calibration standards (RT at 14.53 min); and (d) Similarity search for the
In this four year investigation, more than 50 aerosol associated non-polar organic compounds (NPOCs) were analyzed to identify and assess the dominant aerosol sources in Delhi region. The NPOCs like n-alkanes, PAHs and isoprenoid hydrocarbons present in the aerosol samples, have already been discussed in detail (Yadav et al., 2013b), where the established diagnostic parameters and the Molecular Diagnostic Ratios (MDRs) were used to find out the contributions arising from the biogenic, petrogenic and pyrogenic activities. These investigations revealed an important information about the involved active sources in the region, but the alarming concentrations of nicotine present in the aerosol samples, accompanied with other cigarette smoke markers like quinoline, isoquinoline and anteiso-alkanes, was important to take note of. This stirred us to evaluate the aerosol associated ETS contributions in a focused manner in Delhi's ambient environment. Wartman et al. (1959) had described cigarette smoke as a dense aerosol, and reported predominant presence of the important organic tracer nicotine in the aerosol samples. The use of cigarette smoke specific
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organic markers like nicotine, solanesol, n-alkanes, iso-alkanes, anteiso-alkanes, PAHs, Dicarboxylic acids, quinoline, isoquinoline, also accompany nicotine as a abundantly resolved organic compound among the outdoor cigarette smoke markers (Rogge et al., 1994).
monthly average nicotine concentrations with the Dilution Correction Factor (DCF), where the respective month's DCF value was calculated as (Yadav et al., 2013a, 2013b):
3.1. Profile of ETS associated nicotine
The DCF scaled monthly average nicotine mass concentrations indicate the actual influx or efflux of aerosol associated nicotine, independent of PBL variation (or independent of the changes in the mixing volume). The monthly means of the scaled nicotine concentrations (ng m3) manifested a different temporal profile (Fig. 2, Panel B): higher emissions during April (2007), MarcheAprileMay (2008), and AprileMayeJune (2009) were noted; however, no such pattern was evident for 2006. The mass fraction (ppm) of nicotine in respective samples, with respect to PM10 load (mg m3) is shown in panel C of Fig. 2. Impact of seasonal variations in the temporal plot of nicotine (ppm) is evident. The sample corresponding to November, 2009 requires a special mention as this sample was collected during an episodic event: On 29th October, 2009, there was a fire outbreak in a Indian Oil Corporation Depot located in Sitapura Industrial Area, Jaipur, Rajasthan (located approximately 265 km south west of Delhi). The blaze continued till 11th November, 2009 and the immense fossil fuel burning vitiated the air quality of Delhi region (Singh et al., 2010). In November, 2009, the unusually high nicotine mass concentration, and scaled nicotine concentration was noticed (Panel A and Panel B of Fig. 2). It is reasonable to infer that this peak does not arise due to an excessive smoking on that particular day; it may be due to the chemical reaction between nicotine and other fossil fuel burning markers, or the transported aged nicotine associated with the aerosol particles into Delhi region's environment. However, the mass fraction of nicotine (ppm) in Panel C does not reflect a very prominent peak, implying that the nicotine mass fraction was quite less in comparison to other aerosol associated organic and inorganic compounds (Yadav et al., 2013b). Apart from nicotine, ETS contains complex entity of compounds like pyridines, indoles, quinolines, aromatic hydrocarbons etc. (Rogge et al., 1994; Schauer et al., 2007); the presence of quinoline and isoquinoline compounds in most of the analyzed aerosol samples in the present study validates the above stated conclusion that the traces of cigarette smoke contributes significantly to Delhi's ambient environment.
Nicotine, a main constituent of ETS, is present in both the gas and particle phase (Lofroth, 1995; Pankow, 2001). Investigations reveal that nicotine in a fresh ETS is found in a gaseous phase, however as the ETS ages with time, the gas phase nicotine gets associated with the ambient aerosols (Caka et al., 1990; Lofroth, 1995). The mass concentrations of the aerosol associated nicotine determined in the present investigations are shown in Table 1. The table also compares these values with that reported in earlier studies, though in a different set up and conditions. Nicotine, noticeably, registered a highest individual concentration for all other organic compounds analyzed in this study; the mean nicotine concentration was 448 ± 260 ng m3 (average of the monthly mean concentrations over four years). The time dependent variations in the mass concentrations of nicotine (ng m3), scaled nicotine concentrations (ng m3) and the mass fractions of nicotine (ppm) over four year period are shown in Fig. 2(AeC). The scaled nicotine concentrations takes into account the variations in the mixing volume due to the variation in the planetary boundary layer (PBL) height as explained later in this section. Similarly, the mass fraction concentrations are normalized with respect to the aerosol load of the sample to make it independent of the variations in the aerosol load. The monthly profiles plotted in Panel A of Fig. 2 suggests that the mass concentration of nicotine peaks in the winter months e October, December and November for the year 2006, 2008 and 2009 respectively. The month of April in year 2007, encountered the maximum nicotine concentration, whereas in the same month a minimum concentration was observed in the year 2006. It can be inferred that the monthly mean concentrations of nicotine are found to be governed by the variations in source emissions. Mean mass concentrations of nicotine (ng m3) were 516 ± 302 (2008) > 494 ± 301 (2009) > 438 ± 250 (2007) > 325 ± 149 (2006), suggesting a substantial increase in the tobacco smoking activity over four years. The mass concentration of aerosol associated nicotine (ng m3), in an individual sample, would depend on the mixing volume of the air in which it is present. The mixing volume of air consequently would impact the mass concentration of aerosol, and it follows that the measured concentration of the associated nicotine would need normalization with respect to the variations in the mixing volume; i.e. variations in the PBL. Consequently, to account for this variation we calculated scaled nicotine concentrations by multiplying the
Table 1 Comparison of Nicotine concentration with earlier reported data. S. No.
Experimental setup/source
Nicotine concentration
Reference
1. 2.
PM10 associated nicotine Cigarette smoke collection (1 m3/min) by High volume sampler Ambient nicotine level in common area Cigarette smoke Household dust
448 ± 260 ng m3 467 ± 144 (mmol/g)
Present study Benner et al. (1989)
12.6e60.3 mg m2
Thompson et al. (1989) Rogge et al. (1994) Whitehead et al. (2009) Hoh et al. (2012)
3. 4. 5. 6.
Settled house dust in Smoker homes
1159 mg/cigarette Upto 35,000 ng/g 58.8 mg m2
DCFmonth ¼
Monthly average PBL Annual average PBL
3.2. n-alkanes associated with ETS Considerably high emission rates (549 mg/cigarette) of total nalkanes present in cigarette smoke related aerosol emissions is known (Rogge et al., 1994). It is also established that the cigarette smoke related aerosols display a unique profile of the present nalkanes, where a dominance of odd numbered congeners (C27, C29 and C31), and the most abundant n-alkane (Cmax) at C31 was evident. A similar profile was observed when the contributions to the ambient aerosols also include material arising from leaf abrasion, road dust, garden soil etc.; the presence of n-alkanes in ETS is believed to be from the tobacco leaf surface wax (Rogge et al., 1994; Schauer et al., 2007). It is pertinent to mention here that the homologous series of n-alkanes (C11eC35) investigated in this study does reflect the reported profile: an overall dominance of C27, C29 and C31 congeners was noticed in the ambient aerosol samples with Cmax corresponding with C29 and C31 over this study period. But, a weak correlation (0.125) between homologous series of total n-alkanes and nicotine suggests that the predominant n-alkanes present in the aerosols arose from sources other than the tobacco smoke; only a small contribution may come from cigarette smoke
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Fig. 2. Plotted monthly mean values of nicotine concentration (ng m3); scaled values of nicotine (ng m3) to PBL modulations; and mass fractions of nicotine (ppm) are shown in panel [A], [B] and [C] respectively.
in Delhi region's ambient environment (Fig. 3). The notable presence of isoalkanes and anteisoalkanes in the aerosol samples further supports the argument that the cigarette smoke is also an important source of aerosol associated n-alkanes in Delhi region; similar to the findings reported from Los Angeles (Hildemann et al., 1991).
3.3. PAHs associated with ETS In addition to nicotine's presence in the aerosol samples, the presence of PAHs was significant among the non-polar organic compounds (Yadav et al., 2013b). The concentration of particulate PAHs over four year period was 373 ± 197 ng m3 (2006); 287 ± 98 ng m3 (2007); 410 ± 206 ng m3 (2008); 536 ± 260 ng m3 (2009). The presence of 4-ring PAHs (Fluoranthene and Pyrene) and 6 ring PAHs [Benzo (ghi) perylene] indicated their contributions arising from aerosol associated ETS activity (Lu and Zhu, 2007; Slezakova et al., 2009). Tobacco burning emits all 16 criteria PAHs, but only the concentrations of specific PAHs like chrysene, fluoranthene, benzo(a)anthracene, anthracene,
pyrene, and phenanthrene are reported to be significantly high in ETS (Shihadeh and Saleh, 2005; Ding et al., 2006, 2007; Moir et al., 2008; Hoh et al., 2012). PAHs can also be emitted by other natural and anthropogenic sources involving incomplete combustion, therefore to determine the exact amount of PAHs originating from only cigarette smoke is complicated. A weak correlation between total PAHs and nicotine (0.174) implied that only a small contributions of PAHs are part of ETS (Fig. 4) and their dominant contribution and presence in the aerosols comes from other sources (vehicular exhaust, biomass burning) in Delhi. A weak correlation (0.105) between nicotine and PAHs concentrations is also reported earlier (Hoh et al., 2012).
3.4. Temporal variability in nicotine concentration over four years (2006e2009) The presence of a temporal variability over four years is evident from the plotted monthly average concentrations of nicotine (Fig. 2(AeC)): (i) Concentration in ng m3; (ii) DCF based scalednicotine values; and (iii) the mass fraction of nicotine
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(ng m3). In this analysis, we assume that the monthly mean concentrations can be represented as a linear combination of seasonal variability, trend and random noise; the trend itself is a combination of linear trend and inter-annual variability e the essence is captured by following two equations.
Monthly mean concðtÞ ¼ Seasonal variationðtÞ þ TrendðtÞ þ Random noiseðtÞ (1) TrendðtÞ ¼ Linear trendðtÞ þ Inter-annual variationðtÞ
Fig. 3. Calculated linear correlation between total n-alkane (ng m3) and nicotine (ng m3) mass concentrations present in PM10 load over four year time span (2006e2009).
respectively. The observed variability may be induced by multiple natural factors and temporal variations associated in the involved sources activity. It was worthwhile to extract and assign meaning to the presence of multi-scale variability in the time series of particulate associated nicotine concentrations in terms of underlying physical processes (seasonal or inter-annual influences) Additive Time-series Decomposition (Khalil and Rasmussen, 1990; Tandon and Attri, 2011) and Ensemble Empirical Mode Decomposition (EEMD) based analysis (Huang et al., 1998; Wu et al., 2011; Tandon et al., 2013) was done on the stated time-series data of the nicotine. The methods, besides extracting the embedded variability in the nicotine, also determined the trend present in the particulate nicotine samples collected over the study period (2006e2009). 3.4.1. Determination of seasonal variability and trends in aerosol bound nicotine using additive time series decomposition analysis Additive time-series decomposition analysis was performed on the monthly mean concentration of aerosol bound nicotine
Fig. 4. Calculated linear correlation between total PAHs (ng m3) and nicotine (ng m3) mass concentrations present in PM10 load over four year time span (2006e2009).
(2)
Detailed methodology of the additive time-series decomposition analysis is discussed elsewhere in detail (Tandon and Attri, 2011; Tandon et al., 2012). In Fig. 5A, contributions, with associated standard deviations in the estimated seasonal cyclic variations in the monthly mean concentration of nicotine are plotted as anomalies. The significant standard deviation is associated with the seasonal cyclic variations plotted as the mass concentration of nicotine (ng m3) from Jan 2006 to Dec 2009. Positive nicotine contributions are registered for the post monsoon and winter months (October to January), and negative contributions occur during the monsoon (June to September). During winter months, an expected decrease in the height of the planetary boundary layer would enhance the concentration of both PM10 and associated nicotine/volume. Whereas, during the monsoon, both PM10 and associated nicotine washout is expected, as nicotine has a tendency to attach as a salt on to the €ger and Niessner, 1997). Positive conacidic aerosol particles (Ha tributions with associated large standard deviation noted during April can be attributed to high nicotine bound PM10 load sourced from the dust-storm assisted re-suspension of the surface deposited aerosols (Tandon et al., 2008). In Fig. 5B, a linear trend (linear regression fit) estimate for; a) original monthly mean concentration of PM10 associated nicotine, and b) de-seasonalized monthly mean concentration of PM10 associated nicotine are plotted. Almost a perfect overlap of the linear trend lines is observed for the deseasonalized monthly mean time-series data-set and that for the original data-set. The error estimates with the calculated trend at 95% confidence for the deseasonalized monthly mean time-series data-set was significantly less than the error estimates obtained for the trend line fitted to the original data (Fig. 5B). It is interesting to note that the mass concentration of nicotine present in the ambient aerosol samples increased at the rate of 5.4 ng m3 month1; an increase of ~16.3%/ year. This noticeably implied that tobacco smoking activity in Delhi region has increased between 2006 and 2009.
3.4.2. Extraction of variability and non-linear trend using EEMD One of the limitations of determining the embedded trend is linked with the presence of temporal variability (modulations), non-linearity and departure from the stationarity in the time series data set, like four year nicotine data used in the present analysis (Tandon and Attri, 2011). EEMD analysis circumvents these limitations by allowing the stepwise extraction of multi-scale Internal Mode Functions (IMFs), reflecting cyclic variability present in the data; each IMF differs in terms of period (frequency) and magnitude. Subsequent to the extraction of all IMFs the residuals correspond to the trend present in the data set. Both, the extraction of IMFs and the determination of the embedded trend are tested for their statistical significance. The sensitivity analysis of IMFs and estimated trend is also determined by adding different magnitude of random white noise to the data-set before its EEMD analysis. The details of the underlying philosophy, and the steps required are
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Fig. 5. Calculated monthly variability plotted as histograms from the nicotine time series in panel (A). In panel (B) the calculated linear trend (black line) is plotted using a deseasonalized time series of nicotine (black points).
Fig. 6. Statistically significant ensemble average IMF2 having 6 month period is plotted in panel [A] of the Figure (thick black line); the surrounding redlines show the sensitivity of the estimated IMF2 timeline at 95% confidence. Panel [B] plots the ensemble average trend line (thick black line) drawn through the nicotine monthly average values (ng m3) over four year time period (2006e2009). The surrounding redlines represents the sensitivity of determined trend at 95% confidence.
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discussed in detail elsewhere (Huang et al., 1998; Wu and Huang, 2009; Wu et al., 2011; Chen et al., 2013; Kuo et al., 2013; Tandon et al., 2013; Wu et al., 2011). EEMD analysis of the nicotine timeseries data extracted four IMFs, only IMF2 was found to be statistically significant as per the established criteria (Wu and Huang, 2009; Wu et al., 2011). The noise assisted analysis of 5 different sets of nicotine time-series (Wu and Huang, 2009), enabled the sensitivity analysis of each IMF, and that of calculated embedded trend (Fig. 6A and B). The period of IMF2 (Internal mode function having second highest frequency and representing cyclic variation in the data) determined using Fourier analysis was 6 month (Fig. 6A). A close scrutiny of the considered meteorological variables suggested that similar variation also occurs in PBL (planetary boundary layer) over 6 month which is reflected in the determined IMF2 (i.e. statistically significant Internal Mode Function 2). This cyclic variability in PBL may introduce to a large extent the 6 monthly variability observed in the timeline trend profile of nicotine (Fig. 2A). It is reasonable to infer that the earlier stated rationale to normalize the experimentally determined nicotine profile (Fig. 2A) by using DCF, which accounts for the PBL induced changes in the mixing volume is necessary. The average embedded trend and the sensitivity range determined using EEMD is shown in Fig. 6B, the trend is plotted along with the trend profile of nicotine. The trend timeline over four years is non-linear and the rate of change registers a sharp increase with time. Statistically, the determined trend sensitivity spread region is significantly lower than that observed for the regression based linear trend obtained for de-seasonalized data set of nicotine (Fig. 5B). Unlike linear trend, the non-linear trend provides better appreciation of the change in the timeline profile of nicotine trend associated with the ambient aerosol load over four years. 4. Conclusions Detection of nicotine bound to PM10 load, an important marker compound present in ETS, can reflect qualitative appraisal of tobacco smoking activities. Annual average mass concentrations of nicotine (ng m3) present in the aerosol samples collected over four years were 516 ± 302 (2008) > 494 ± 301 (2009) > 438 ± 250 (2007) > 325 ± 149 (2006). The presence of nicotine and ETS associated other organic compounds (n-alkanes, iso-alkanes, anteiso-alkanes, PAHs, Isoquinoline etc.) helps in assessing the ETS activity and partitioning of associated compounds from gaseous to particle phase (aerosol matrix) in the surrounding environment. The poor correlation shown by nicotine with total nalkanes (0.125) and nicotine with total PAHs (0.174) suggests that most of the aerosol associated total n-alkane and total PAHs in the region does not come from tobacco smoke. The timeline profile of nicotine present in collected aerosol samples, over four years, was subjected to Additive Time-Series Decomposition, and Ensemble Empirical Mode Decomposition to extract the seasonal variability and embedded trend. The estimated linear trend of 5.4 ng m3 month1 corresponds to 16.3% per annum increase in PM10 associated nicotine. EEMD analysis based determined nonlinear trends, too, indicated a steep increase in nicotine present in ambient aerosol samples collected in Delhi region between 2006 and 2009. The survey reports establish that in excess of 30% of the population above the age of 15 years either smoke or chews tobacco (Rani et al., 2003; Ng et al., 2014), and if the increase in the concentration of particulate nicotine, as reported in present study is taken into account (non-linear trend) then it can be inferred that the total number of tobacco users in Delhi region are on the rise. In the absence of any data on tobacco consumption from 2006 to 2009 from Delhi region, to validate the calculated 16% per annum increase in the nicotine associated with PM10, the indirect estimates
were derived. All India figures of the industrial production of tobacco products from 2006 to 2009 were taken to represent the national consumption (Economic Survey, 2011). The state wise estimates during 2006e2009 for Delhi region show that 24% of the total population smoke tobacco products in one form or other, whereas the proportion at India level stands at 35% (GATS survey, 2011). On the assumption that the manufactured tobacco products are all consumed, the Delhi region's consumption can be derived in proportion to the population smoking tobacco products nationally, and in Delhi region between 2006 and 2009. The estimated linear trends from this indirectly derived data for Delhi region was 10.4% per annum; the figure is close to the estimated trend of 16% calculated from the nicotine bound to PM10. The difference between two figures may arise on account of the re-suspension of the surface deposited nicotine associated aerosol load back into the environment's PM10 load having nicotine. Findings from this study pose a question on the societal efforts to eradicate tobacco smoking menace? Measures are required to control the use of tobacco; otherwise the economic burden to tackle the expected increase in the ETS related diseases will be enormous. The investigation presented also emphasize that the detection of nicotine present in ambient particulate load may act as a useful tool to appraise qualitative assessment of ETS activities. Acknowledgments Authors extend their gratitude to the financial support provided in the form of a project by Council for Scientific and Industrial Research, India. SY acknowledges financial assistance provided by the University Grant Commission in the form of Senior Research Fellowship. AT is thankful to the Department of Science and Technology, Government of India for the financial assistance provided to him, in the form of a research project for young scientists. We thank Advanced Instrumentation Research Facility, Jawaharlal Nehru University, for TD-GC-MS facility. References Benner, C.L., Bayona, J.M., Caka, F.M., Tang, H., Lewis, L., Crawford, J., Lamb, J.D., Lee, M.L., Lewis, E.A., Hansen, L.D., Eatough, D.J., 1989. Chemical composition of environmental tobacco smoke. 2. Particuiate-phase compounds. Environ. Sci. Technol. 23, 688e699. Caka, F.M., Eatough, D.J., Lewis, E.A., Tang, H., Hammond, S.K., Leaderer, B.P., Koutrakis, P., Spengler, J.D., Fasano, A., McCarthy, J., Ogden, M.W., Lewtas, J., 1990. An intercomparison of sampling techniques for nicotine in indoor environments. Environ. Sci. Technol. 24, 1196e1203. Chen, X., Zhang, Y., Zhang, M., Feng, Y., Wu, Z., Qiao, F., Huang, N.E., 2013. Intercomparison between observed and simulated variability in global ocean heat content using empirical mode decomposition, part I: modulated annual cycle. Clim. Dyn. 41, 2797e2815. Da Silva, F.R., Da Silva, J., Allgayer, M.Da.C., Simon, C.F., Dias, J.F., dos Santos, C.E.I., Salvador, M., Branco, C., Schneider, N.B., Kahl, V., Rohr, P., Kvitko, K., 2012. Genotoxic biomonitoring of tobacco farmers: biomarkers of exposure, of early biological effects and of susceptibility. J. Hazard. Mater. 225e226, 81e90. Ding, Y.S., Yan, X.J., Jain, R.B., Lopp, E., Tavakoli, A., Polzin, G.M., Stanfill, S.B., Ashley, D.L., Watson, C.H., 2006. Determination of 14 polycyclic aromatic hydrocarbons in mainstream smoke from U.S. brand and non-U.S. brand cigarettes. Environ. Sci. Technol. 40 (4), 1133e1138. Ding, Y.S., Ashley, D.L., Watson, C.H., 2007. Determination of 10 carcinogenic polycyclic aromatic hydrocarbons in mainstream cigarette smoke. J. Agric. Food Chem. 55, 5966e5973. Economic Survey 2010e11, 2011. Statistical Appendix. Government of India. Available at: http://indiabudget.nic.in. Global Adult Tobacco Survey, 2009e2010, 2010. Ministry of Health and Family Welfare. Government of India. Available at: http://mohfw.nic.in/ WriteReadData/l892s/1455618937GATS%20India.pdf. €ger, B., Niessner, R., 1997. On the distribution of nicotine between the gas and Ha particle phase and its measurement. Aerosol Sci. Technol. 26 (2), 163e174. http://dx.doi.org/10.1080/02786829708965422. Hildemann, L.M., Markowski, G.R., Cass, G.R., 1991. Chemical composition of emissions from urban sources of fine organic aerosols. Environ. Sci. Technol. 25, 744e759.
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