Background

Wastewater-based epidemiology (WBE) is an innovative approach for estimating population-level exposure to a wide range of substances (such as drugs, chemicals, biological agents), that is based on the human excretion of selected biomarkers into wastewater (Markosian et al., 2019). Wastewater-based epidemiology was first proposed by Daughton (Daughton, 2001) in 2001, and further developed, incorporated, and implemented into the present-day field of WBE by Zuccato et al. in 2005, and subsequently by others (Montes et al., 2020; Orive et al., 2020; Pei et al., 2016; Senta et al., 2015; Sodre et al., 2018; Yadav et al., 2019). To date, its most prominent application is in the estimating illicit drug consumption. Numerous WBE studies have been widely conducted, to elucidate the spatial and temporal trends in the illicit drug consumption in many regions, including Europe, Africa, Australia, the Netherlands, and China (Archer et al., 2018; Bade et al., 2018; Causanilles et al., 2017; Gao et al., 2017; Ort et al., 2014). Moreover, WBE has been applied to assess health status (Gao et al., 2018; Zheng et al., 2017), monitor outbreaks of infectious diseases, and estimate the prevalence of diseases, including chronic hepatitis B and diabetes mellitus (Hou et al., 2020; **ao et al., 2019). Furthermore, WBE has been used to detect of SARS-COV-2 in wastewater, suggesting the potential utility of WBE for COVID-19 community surveillance, following by infectious diseases such as poliovirus and noroviruses in wastewater (Orive et al., 2020; Shah et al., 2022). This approach is low-cost, convenient, and efficient, and can provide more objective results in near real-time than the use of traditional surveys, such as census data, and questionnaire-based surveys (**ao et al., 2019; Zheng et al., 2020).

However, uncertainty could be introduced in different steps involved in estimating wastewater analysis, such as biodegradation and/or transformation of compounds in wastewater, temperature, and in fecal and/or urinary excretion of compounds. The highest uncertainty in the WBE approach, which could be as high as 7–55%, is the estimation of the population size (Castiglioni et al., 2013; Yu et al., 2021). The use of appropriate correction factors is recommended to reduce uncertainty and improve the comparability of results. Therefore, accurate estimations of the corresponding populations are particularly important. To date, majority WBE studies have used the population statistics obtained from wastewater treatment plant (WWTP) design capacities, hydrochemical parameters, and census data (Hou et al., 2021, O'brien et al., 2014, Zheng et al., 2019). However, population figures based on design capacity are an unreliable parameter because WWTPs can operate below or above their design capacities, leading to deviation from the actual population sizes. Census data are based on fixed population sizes, according to home addresses, which are not regularly updated and do not consider changes owing tourism or commuting patterns (Rico et al., 2017). Industrial discharges, agricultural activities, or food waste could also affect hydrochemical parameters such as total phosphorous (TP), total nitrogen (TN), biological oxygen demand (BOD), and chemical oxygen demand (COD) mass loads, resulting in the overestimation of population sizes (Hou et al., 2021; Rico et al., 2017). For instance, COD, BOD, and TP are easily affected by industrial production, and TN is mainly affected by derived from agricultural activities, or food waste. Therefore, total TP, TN, BOD, and COD are also unsuitable to estimate the population sizes (Kumar et al., 2019). Similarly, accurate estimation of the population by using metal ion as biomarkers is suspected because of possible non-human sources of contribution to the wastewater. Various pharmaceuticals and personal care products (PPCPs), such as carbamazepine, cholesterol, creatinine, acesulfame and caffeine, have also been widely suggested as population biomarkers (PBs) (Chen et al., 2014, Daughton, 2012, O'brien et al., 2014, Rico et al., 2017, Senta et al., 2015, Tscharke et al., 2019). However, they are not suitable as biomarkers because they are not stable in wastewater, are easily absorbed, and/or have visible seasonal changes. Moreover, the sales data for the selected pharmaceuticals and per capita consumption are not always accurate (Hou et al., 2021).

Recently, human endogenous substances such as coprostanol, cholesterol, cortisol, androstenedione, creatinine, 5-HIAA, and the catecholamine metabolites, homovanillic acid (HVA), and vanillylmandelic acid (VMA) have been proposed for use as PBs in WBE (Chen et al., 2014, O'brien et al., 2019, Pandopulos et al., 2019, Rico et al., 2017, Thai et al., 2019). The use of human endogenous substances-based population has been shown to be the most accurate for population estimations. Therefore, it is necessary to further study the types of human endogenous population biomarkers.

Theoretically, the use of human metabolic biomarkers is a relatively accurate method for estimating population sizes (Lin et al., 2019). K, an endogenous substance, is an essential nutrient with physiological functions that is stably excreted by the human body in urine. It is the most abundant cation in intracellular fluid, where it plays a key role in maintaining cell functions, particularly in excitable cells such as muscles and nerves (Stone et al., 2016). The World Health Organization recommends that all adults should increase their K intake to ≥ 3.5 g per day (WHO, 2012)

Metal ions and minerals are essential compounds that support a variety of important biological functions for daily human life, are closely related to human production, and are mainly excreted into the wastewater system through urine (Stone et al., 2016; Van Dronkelaar et al., 2018). Based on these features, we propose a novel method that utilizes metal ions as biomarkers to accurately estimate population parameters in WBE. Reportedly, our study is the first to apply the metal ion Kjavascript: as a potential PB for wastewater analysis. K meets the PB candidate criteria: quantification method, affinity to particulate matter, stability of candidates in wastewater, consistency in inter-day excretions, and correlations between total excretion and census population (Chen et al., 2014). Sequentially, the consumption of tobacco was estimated using the developed K ion PB candidate in 24 cities (234 WWTPs) in southern China. Monte Carlo simulations were also performed to estimate the uncertainties. The results were then compared with official survey reports and other population estimation studies, to obtain a better understand the tobacco consumption in China.

Materials and Methods

Chemicals and reagents

High-performance liquid chromatography (HPLC)-grade acetonitrile (ACN) and methanol (MeOH) were obtained from TEDIA (Ohio, USA). Nitric acid (GR, approximately 68%) and perchloric acid (GR, approximately 72%) were purchased from Nan**g Chemical Reagent Co., Ltd. (Nan**g, China). Ultrapure water was produced using a water purification machine from a Millipore purification system (Millipore, MA, USA). Analytical standard cotinine (purity > 98%) and isotopic internal standard cotinine-d3 (purity > 98%) were purchased from Cerilliant (Texas, USA). Sodium standard (Na, 1 mg/mL), potassium standard (K, 0.1 mg/mL), calcium standard (Ca, 1 mg/mL), magnesium standard (Mg, 0.1 mg), iron standard (Fe, 1 mg/mL), and zinc standard (Zn, 1 mg/mL) were purchased from the National Research Center for Standard Materials (Bei**g, China). Lanthanum (III) oxide (purity ≥ 99.99%) was supplied by Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). Cesium chloride (purity ≥ 99.99%) was obtained from Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). Standard compounds were stored following to the supplier’s recommendations. Stock solutions of individual compounds were prepared in water or MeOH, and the calibration solutions were diluted from individual stock solutions.

Sample collection

In 2019, daily composite influent wastewater samples were collected from 234 selected municipal WWTPs across China (Shanghai, Hangzhou, Nanchang, Hefei, Fuzhou, Nan**g, Huzhou, Jiaxing, **hua, Ningbo, Taizhou, Wenzhou, Quzhou, Shaoxing, Zhoushan, Lishui, **amen, Haikou, Chengdu, Chongqing, Kunming, Guiyang, Changsha, and Zhengzhou), which serviced the entirety of these metropolitan areas and their populations and represented the real-world conditions of these cities (Fig. S1). Twenty-four-hour composite samples of raw wastewater were collected from influent using an LB-8000D automatic water auto-sampler (Nan**g Yongda Electromechanical Technology Co. Ltd., Nan**g, China) with a sampling frequency of once every hour. Samples were collected on sunny or cloudy days to minimize the influence of rainfall on the flow volume of the WWTPs. All the samples were collected in 500 mL polyethylene terephthalate (PET) bottles, acidified to pH = 2.0 using hydrochloric acid and then frozen at − 20 °C until analysis. The sampling information is presented in Table S1.

Apparatus and analytical methods

The metal ions in the wastewater samples were determined by aspirating into an acetylene flame on an atomic absorption spectrometer ( AAS; AA-7000, Shimadzu, Japan), after modified fast Kjeldahl digestion. The key parameters of the instrument were set as follows: acetylene gas flow, 2.0 L/min; air flow, 15 L/min; atomizer height, 7.0 mm. The chemical flames provided energy to atomize the metal ions, their atoms absorbed characteristic electromagnetic radiation, and the metal ions were then quantified. The hollow cathode lamp current ranged from 2–15 mA, and the voltage was ≤ 360 V. The Na, K, Ca, Mg, Fe, and Zn concentrations were calculated automatically using the corresponding linear calibration method for each ion, based on their relative absorbances with the Lab solution Spectra AA software.

The amount of cotinine in the wastewater was determined using an ultra-high-performance liquid chromatography-mass spectrometer (UHPLC-MS/MS) system (TSQ Quantis, Thermo Scientific, San Jose, CA, USA) equipped with an electrospray ionization interface, operated in positive (3.5 kV) ion mode and the multiple reaction monitoring (MRM) mode. The UHPLC-MS/MS parameters established for the MRM acquisition of cotinine and cotinine-d3 are shown in Table S2. Chromatographic separation was performed with an Agilent Eclipse Plus C18 column (50 × 2.1 mm, 1.8 μm), set at 40 °C, with linear binary gradient elution; using water containing 0.10% formic acid as mobile phase A and acetonitrile as mobile phase B at a flow rate of 0.3 mL/min. The elution gradient was set as follows (A: B): 0 min (95:5) → 6 min (75:25) → 6.2 min (0:100) → 8 min (0:100) → 8.1 min (95:5) → 11 min (95:5).

Sample preparation

A total of 234 WWTPs with 3–4 wastewater samples were collected from each WWTPs at different time point. The wastewater samples were pretreated via modified fast Kjeldahl digestion, before metal ion determination, as described below. Unfiltered raw wastewater was digested to prevent underestimating from analyte absorption by particulate materials or biofilms. A 50 mL aliquot of wastewater sample was accurately measured and mixed in a 100 mL beaker with 3 mL nitric acid. The mixture was then digested by gently boiling it on a hotplate at 90–100 °C for approximately 1 h, until a clear and transparent solution and a volume of approximately 20 mL was obtained. After cooling to 20 ± 5 °C, 2 mL of perchloric acid was carefully added, and the solution was gently boiled and evaporated to approximately 1 mL.

The digested solution was then cooled to room temperature, transferred to a 50 mL volumetric flask, and quantitatively diluted to volume. The blank pure water solution was pretreated in the same manner. Samples with total levels exceeding their linear range were diluted to the appropriate concentrations for AAS analysis. To overcome the sensitization effect between Na and K, the metal ion cesium, which was more easily ionized than Na and K, was added to the samples as an ionization buffer, so that Na and K could continue to be determined in the same sample. Similarly, when Ca and Mg were being determined, lanthanum was added to eliminate the interfering effects of aluminum, sulfate and phosphate.

Before cotinine was analyzed, the wastewater samples were prepared using solid-phase extraction (SPE). First, all the sewage water samples were filtered through GE GF/C glass microfiber filters (GE Whatman, Kent, UK) to remove solid particles. Then, an aliquot of 50 mL of each filtered sewage water sample was accurately measured, spiked with 100 μL of deuterated IS (50 ng/L), and pretreated with an Oasis-MCX cartridge for cotinine determination. Briefly, the SPE procedure was performed as follows. Before use, the SPE cartridges were conditioned with 4 mL methanol washing and 4 mL pure water rinsing; then, the spiked 50 mL sewage water sample was loaded at a speed of 2 mL/min. After loading, the cartridge was washed with 4 mL methanol, and the trapped cotinine was then eluted using 4 mL of a mixture of ammonia solution (concentration: 25–28%) and acetonitrile (v/v 5:95) at a flow rate of 1 mL/min. The collected eluent was evaporated under reduced pressure using centrifugation at 35 °C to approximately 0.4 mL, then acidified with 20 μL mixture of hydrochloric acid (concentration: 36–38%) and acetonitrile (v/v 5:95), and further evaporated until dry. The residue was reconstituted with 100 μL of methanol containing 0.10% formic acid through vortex mixing. After centrifugation at 13,000 × g for 10 min, the resultant supernatant was transferred into an auto-sampler vial, and an aliquot of 10 μL was injected into the UHPLC-MS/MS system for the determination.

Preparation of calibration standards

The series of working standard solutions in the linear range for AAS are shown in Table S3. The calibration range of cotinine, as determined using UHPLC-MS/MS, was 10–5000 ng/L (10, 50, 125, 500, 1250, 2500, and 5000 ng/L). A cotinine-d3 internal standard was used to minimize the influence of matrix effects. Quality control (QC) was conducted to ensure the accuracy of the cotinine quantification method. Three different concentrations (25, 400, and 4000 ng/L) were selected as QC concentrations for method validation, and the relative standard deviation (RSD) between the measured and the given concentrations of QC samples was within ± 15%.

Method validation

Wastewater was evaluated using the validated AAS method. To evaluate the performance of the proposed technique, precision, linear dynamic ranges, stability, recovery, limit of detection (LOD), and limit of quantification (LOQ) were investigated by utilizing standard solutions of metal ions in ultra-pure water. The LOD and LOQ, defined as LOD = 3SD/S and LOQ = 10SD/ST (where SD and S are the standard deviation of the blank and slope of the calibration curve), and the obtained results are summarized in Supplementary Table S3. The validated results indicated that the established method was suitable for the determining metal ions in wastewater.

Calibration curves prepared in wastewater were linear at a concentration range of 10–5000 ng/L with the correlation coefficient (r2) of 0.9994 for cotinine. The LOQ was determined to be 25 ng/L based on a signal-to-noise ratio (S/N) of 10 and the LOD was 10 ng/L (S/N = 3). Recovery of cotinine was 106.91% ± 3.28%, which means the recovery could be corrected using the isotopically labelled internal standard. Meanwhile, the matrix effect was 94.49% ± 8.61%. The other results of precisions and accuracy for the UHPLC-MS/MS analytical methods are presented in Supplementary Table S4.

Population based on inorganic metal ion biomarker in wastewater

Metal ions were used as indirect PBs. Population size was estimated using the following equation:

$$P_{{{\text{PB}}}} = \frac{{ C_{{{\text{PB}}}} \times F}}{{M_{{{\text{PB}}}} }}$$
(1)

where CPB is the influent concentration (mg/L) of the PB of the WWTP, F is the daily flow volume (L/day) of wastewater in WWTPs, and MPB is the daily excretion of PB per capita for the metal ion under investigation (Na, K, Ca, Mg, Fe, Zn).

Table 1 Monte Carlo parameter distributions of other input parameters for estimation of tobacco use
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Back calculation of nicotine and tobacco consumption

To estimate nicotine consumption, the detected concentrations of cotinine were combined with the wastewater flow volume, correction factors, and population sizes served by the respective WWTPs, to calculate the daily nicotine consumed per capita. The equations used to back-calculate the consumption of nicotine are as follows, according to WBE methodology:

$$m_{{{\text{nic}}}} = \frac{{C_{{{\text{cot}}}} \times F \times f_{{{\text{cot}}}} }}{P}$$
(2)

where Ccot is the concentration (g/L) of cotinine in the WWTP, F is the daily flow volume (L/day), P is the population served by a WWTP, and fcot is the correction factor for cotinine, which can be calculated as follows:

$$f_{{{\text{cot}}}} = \frac{{M_{{{\text{nic}}}} }}{{M_{{{\text{cot}}}} \times X_{{{\text{cot}}}} }}$$
(3)

where Mnic and Mcot are the molecular weights of nicotine and cotinine, respectively, and Xcot (%) is the excretion percentage of cotinine, relative to nicotine.

Therefore, Eq. (2), used to calculate the nicotine consumed, can be simplified as follows:

$$m_{{{\text{nic}}}} = \frac{{C_{{{\text{cot}}}} \times f_{{{\text{cot}}}} \times M_{{{\text{PB}}}} }}{{C_{{{\text{PB}}}} }}$$
(4)

As a biomarker of nicotine intake, cotinine can be used to estimate nicotine consumption. Similarly, we can use nicotine consumption to estimate cigarette consumption, with smokers’ CPD calculated using Eq. 5.

$$n = \frac{{m_{{{\text{nic}}}} }}{{{\text{D }} \times {\text{ Y }} \times {\text{ X}}_{{{\text{smoker}}}} { } \times { }R_{15} }}$$
(5)

where D is the nicotine content of an average cigarette weighing 0.9 mg, as measured from cigarettes in the Chinese market (Wang et al., 2016; Zheng et al., 2017); Y is the average yield of nicotine uptake during smoking (calculated at 66%) (St Charles et al., 2010); R15 is the 86.6% proportion of the population in Zhejiang Province in China over the age of 15 (Zhejiang Provincial Bureau of Statistics, 2021); and Xsmoker is the 26.6% proportion of smokers in China (National Health Commission of the People’s Republic of China, 2021). All the parameter values used in this study are listed in Table 1.

Table 2 The coefficient matrix of population based on metal ions population biomarker in 24 cities
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Uncertainty and sensitivity analyses

The estimation of tobacco consumption was subject to several uncertainties associated with the various steps involved. Therefore, Monte Carlo simulations were conducted estimate the uncertainty and sensitivity in this study, using Oracle Crystal Ball software (11.1.2.4). These were based on repeated random sampling (n = 100,000 samples) from the probability distributions defined for the principal factors of variation and uncertainty of each parameter. The sensitivity analysis indicated the extent to which each predictor variable contributed to the uncertainty or variability of the predictions.

Results and discussion

Occurrence of consumption biomarker cotinine and metal ions in wastewater

Biomarkers for tobacco consumption were detected in the entire set of the analyzed samples (frequency of detection, 100%). The concentration of cotinine detected ranged from 22–2663 ng/L (Table S1). The metal ions were detected and quantified in wastewater samples, and the concentrations are listed in Table S1.

Estimation of population size

The levels of metal ions in human excretion are closely related to human activities and can be used as the basis for population assessments, mainly because their per capita emissions are relatively constant. Therefore, metal ions in human excretion change with variations in populations, which means they can reflect the status of the regional population to some extent. The population served by the WWTPs was estimated by dividing the mass load of these parameters by the literature-reported load of excretion per capita load.

To evaluate the use of metal ions for quantification of population size, the value of 24-h excretion mass loads should be provided accurately to calculate the metal ion equivalent population. Numerous studies have reported that the daily urinary metal ions excretion can vary a small amount for individuals. A study found 24-h urinary sodium and potassium excretion in China to be 189.07 mmol/24-h and 36.35 mmol/24-h, respectively (Tan et al., 2019). In another study, 24-h urinary calcium and magnesium excretion were found to be 0.332 ± 0.122 mg/kg and 0.205 ± 0.037 mg/kg, respectively, in healthy prepubertal boys (Touitou et al., 2010). Meanwhile, considering daily variation and lower industry input surveyed from the WWTPs operators, the average 24-h excretion mass loads of six metal ions were selected as 4.49, 2.98, 0.2, 0.09, 0.005, and 0.00068 g/d/capita, respectively (Table S6) (Diagnostics., 2013).

The censused populations for the WWTPs varied from 10,000 to 7,120,000. The de facto population (metal ion equivalent population) was calculated using Eq. (1) and compared with census data using nonparametric Spearman’s correlation rank analysis to characterize the correlations. The results demonstrated that, except for Zn, there were clear and strong correlations between the target metal ion equivalent population and census data (Table 2). The correlation coefficients (R) were RK = 0.78, RNa = 0.66, RCa = 0.81, RMg = 0.77, and RFe = 0.69, at p < 0.01, showing significant strong positive correlation. However, there was a weak correlation between the Zn equivalent population and census data, which had a correlation coefficient of 0.11, at p > 0.01.

One-way analysis of variance (ANOVA) was used to estimate the differences between metal ions population and census data (Fig. S2). The K equivalent population (p = 0.9999 > 0.05) showed no significant differences compared to census data. The Na, Ca, Mg, Fe, and Zn equivalent populations (p = 0.0001 < 0.01) were significantly different from the census data. The results revealed that K met the requirements for an ideal biomarker for population estimation, as it was easily detected in wastewater and relatively stable in wastewater during the sampling period (within 24-h), and representative of the human metabolism.

Daily consumption of nicotine and tobacco

The linear correlation between the measured cotinine mass loads and the K equivalent population was evaluated. As shown in Fig. 1, the linear regression suggested that there was a strong correlation between cotinine mass loads and K equivalent population (r2 = 0.966), implying that cotinine was suitable as a biomarker for accurate tobacco consumption estimation.

Fig. 1
figure 1

Relationship between the cotinine mass load and K equivalent population

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The excretion of nicotine as cotinine can vary owing the influence of enzymes, age, gender, diet, and medications, all of which can contribute to uncertainty in the excretion factor (Benowitz et al., 2009). When smoking is the main route of nicotine uptake, cotinine is excreted in the urine unaltered, and accounts for 10–15% of the excreted total nicotine equivalent (Hukkanen et al., 2005; Järvinen et al., 2019). These mean excretion rates were used to calculate the amount of nicotine consumed. The daily mass load of nicotine was calculated using Eq. (2). During the sampling period, the daily mass load of nicotine ranged from 0.06 to 7.20 mg/d/capita, and the average was 1.76 ± 1.19 mg/d/capita (Table S5), which was similar that of Australia (1.7 mg/d/capita) and Spain (1.7 mg/d/capita), but much lower than that of Italy (3.4 mg/d/capita), the United States (2.7 mg/d/capita), and Portugal (2.6 mg/d/capita) (Castiglioni et al., 2015; Chen et al., 2019; Lai et al., 2018; Lopes et al., 2014; Rodriguez-Alvarez et al., 2014). Our results were similar to those of the other Chinese studies, this result was similar to China (1.5 ± 0.9 mg/d/capita in 2014 and 1.8 ± 1.1 mg/d/capita in 2016), but much lower than the consumption of nicotine in Jilin (2.39 mg/d/capita) and Dalian (2.0 mg/d/capita) (Gao et al., 2020; Wang et al., 2016; Zheng et al., 2017). The different mass load of nicotine per capita may indicate the different tobacco prevalence in these areas.

The average tobacco consumption in Southern China was 3.45 ± 2.33 CPD, a similar result to that from the Chinese adult tobacco survey conducted in 2015 (3.6 CPD) (Chinese adult tobacco survey, 2015). However, our estimation was lower than the value of 4.7 CPD reported by the 2015 Global Adult Tobacco Survey and the value of 5.6 CPD estimated from the sales statistics extracted from the 2016 national tobacco report (Report of National Cigarette Market in 2016, 2017; Global Adult Tobacco Survey China 2015 Country Report, 2015). For smokers, the average number of cigarettes consumed was 13.0 ± 8.75 CPD in southern China. This result was lower than that of a previous study conducted in Jilin and Dalian in Northern China, which reported 16.0 and 14.6 CPD, respectively (Wang et al., 2016; Zheng et al., 2017). A recent WHO report of 14 Chinese cities in 2013–2014 demonstrated that tobacco consumption in northern China was higher than that in southern China (WHO, 2015), and the difference could be attributed to the cultural, habitual, and economic factors (Gao et al., 2020).

In terms of the daily consumption of cotinine, nicotine and tobacco based on different population estimations (Fig. 2), the K equivalent population was significantly consistent with census population. This indicates that K equivalent population is a particularly effective PB compared with the census population.

Fig. 2
figure 2

Box and whiskers plots for the daily consumption of cotinine, nicotine, tobacco and tobacco for smoker based on K equivalent population and census population, respectively. Line: median; Whisker: max to min; Box: 25% − 75% interquartile range

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Spatial variation

The prevalence of smoking varied in different provinces across China. For instance, the prevalence in Guizhou and Inner Mongolia was > 30.0% (Wang et al., 2019), but only 21.9% and 19.9% in Zhejiang and Shanghai in Southern China, respectively (Chinese Adult Tobacco Survey, 2018). In this study, the lowest per capita tobacco consumption for smokers was found in Quzhou and Ningbo (cities of Zhejiang), at 6.33 and 6.58 CPD, respectively, while Guiyang (capital of Guizhou province) had the highest tobacco consumption of 23.49 CPD (Fig. 3). This agreed with the prevalence of smoking in each catchment area (Wang et al., 2019). These differences in the CPDs may be explained by the corresponding tobacco prevalence, populations, and flows arriving at the WWTPs, the impact of rainwater on wastewater, and the sampling technique.

Fig. 3
figure 3

Map of Southern China summarizing the cities covered estimated tobacco consumption for smokers

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Uncertainty analysis

This estimation was also subject to several uncertainties associated with the various steps involved. The probability distribution density of cotinine, nicotine, and cigarettes consumed is shown in Fig. S3. The Monte Carlo simulation results agreed with the estimations that were based on the mean values of the input parameters described above. The sensitivity analysis showed that the most significant parameters were Ccot and CK, which contributed more than 60.0% and 30.0% of the total uncertainty, respectively (Fig. S4). The results of the sensitivity analysis showed that individual variability plays an important role in the uncertainties of the estimation results. Further studies to improve the estimation of the true values of these parameters should be performed to reduce the uncertainties in the estimates of consumption.

Conclusions

This study systematically evaluated the metal ions as potential population biomarkers for WBE. The results demonstrated that K equivalent population matched well with census population and is suitable for monitoring relative population change. Tobacco consumption based on the K equivalent population was evaluated in southern China and was consistent with the official reports and the recent publications in China, providing a new method for accurately evaluating spatiotemporal trends in tobacco use among accurate populations in communities. Furthermore, K equivalent population was applied to WBE, promoting efficient construction of the government tobacco control department.