Identification Of Pollution Sources Research Articles

Spatial distribution of smelter emission heavy metals on farmland soil.

This work was conducted to explore heavy metal pollution in soils in an area near lead smelters in Jiyuan City, which is one of the main lead production areas in China. Altogether, 88 topsoil samples (0-20cm) were collected from farmlands near the Yuguang lead smelting facilities; the sampling sites were 1570 to 6388m to the main stack of the Yuguang. Analysis of the samples indicated that (i) the ranges of total Cd and Pb concentrations were 0.81-4.30 and 64.5-435mgkg-1, respectively, mean pollution indices (concentration in soil/background value, PI) were 32.8 and 9.11, respectively, and the concentrations of total Cu, Zn, and Ni were slightly higher than the background values. Mean concentrations of DTPA-extractable Cd and Pb were 0.752 and 58.7mgkg-1, respectively. (ii) The total concentrations of Cd, Pb and Pb/Cd ratios of samples decreased as the distance to the main stack of the Yuguang increased. Abnormal variations of these trends suggested these parameters of certain samples were affected by pollution sources other than the Yuguang. (iii) Judged by the results of this work, the area of the heavy metal-polluted land in Jiyuan was much greater than 115km2, a value reported by an earlier investigation. These results indicate that the soil in the study area was polluted by Cd and Pb emissions from more than one polluting sources, the variation of Cd, Pb concentration and Pb/Cd ratios of samples to the distance of the pollution source can be potentially used for pollution source identification.

Wavelet denoising and cubic spline interpolation for observation data in groundwater pollution source identification problems

Abstract As the identified results of groundwater pollution source identification (GPSI) can influence the cost for the polluter in paying for remediating groundwater resources, it is important that the accuracy of the estimated result should be as high as possible. However, many factors can influence the result, such as noisy concentration data and incomplete concentration data. Thus, this paper is aimed at studying the difference between using the observation data before and after denoising and interpolating for solving GPSI problems. Four kinds of noise level and 20 groups of missing data were designed to test the performance of wavelet denoising and cubic spline interpolation, respectively. The results show that the denoising process can improve the estimated result for the GPSI problem, and the higher the noise level, the stronger this effect. In terms of interpolation, more accurate results can be made after interpolating if the missing data belong to the period after the source releases the pollutant. If the missing data are from when the pollution source is active, interpolation cannot help increase the estimated performance.

Spatial distribution and source identification of heavy metals in a typical Pb/Zn smelter in an arid area of northwest China

Heavy-metal pollution has become a serious problem in the vicinity of mining sites. In this study, we investigated the pollution degree and spatial distribution of heavy metals and determined their sources in soil near a typical Pb/Zn smelter in an arid area of northwest China. The pollution degree, spatial distribution and source identification of heavy metals were evaluated and analyzed by the geo-accumulation index (Igeo), enrichment factor (EF), ArcGIS-based kriging interpolation models and multivariate statistical techniques. The results showed that the average concentrations of Pb, Zn, Cd and Cu were higher than their background contents of Shaanxi Province. The results of chemical speciation fractions show that the non-residual fraction (Acid soluble + Reducible + Oxidizable) of Pb, Zn and Cd were greater than the residual fractions of the same elements, while the results of Cr, Cu and Mn were just contrary. The Igeo values demonstrated that the issue of soils contaminated by Pb, Zn and Cd in this area is very serious, where Cd pollution was the most significant. The EF values decrease in the following order: Cd > Zn > Pb > Cu > Cr, and indicating Cd was extremely severe enrichment. The spatial distribution indicated that Pb, Zn, Cd and Cu mainly originated from smelting activities, which were heavily accumulated in topsoils and decreased with increasing soil depth in the profile distribution. The indexes of Igeo and EF showed that Pb, Zn, Cd and Cu were the most important contaminants, which had the same sources, deriving from anthropogenic metal smelting activities. These results can serve as a basis for designing effective strategies to prevent, control and reduce further heavy-metal contamination of the soils. Meanwhile, the relevant study methods, study hypotheses and methodologies may provide certain reference significance for the study of soil pollution from similar heavy metals.

Source Identification of Episodic Rain Pollutants by New Approach: Combining Satellite Observations and Backward Air Mass Trajectories

ABSTRACT In this study, various source apportionment methods, viz., enrichment factor, relative source contribution, and factor analysis in addition to cluster analysis coupled with satellite observations, were used to investigate the origin of rainwater pollutants in an arid urban area of Saudi Arabia. The rainwater samples were collected by an automatic wet-only sequential rain sampler and analyzed for their pH, electrical conductivity (EC), and major ions (Cl–, NO3–, SO42–, HCO3–, Na+, K+, NH4+, Ca2+, and Mg2+). The results revealed that the chemical composition was dominated by Ca2+, SO42–, Cl–, HCO3–, and Na+, which represented 80% of the total ionic equivalent concentration. Ca2+, the most abundant ion, exhibited a mean concentration of 480 µeq L–1 and accounted for 30% of the total ionic equivalent concentration. The study area received a total ionic wet deposition flux (FWD) of 4.07 tons km–2 y–1, and SO42– and NO3– contributed 76% and 24%, respectively, of the rainwater acidity. Ca2+ was responsible for 80% of the rainwater’s neutralization. The source apportionment indicated that intense local human activity and medium- to long-range transport from Kuwait, Iraq, Qatar, Riyadh, and the Western Province produced the majority of the anthropogenic components (SO42–, NO3–, and NH4+), which represented 47% of the total ionic FWD. However, moderate to high particulate matter loads over the southern part of Saudi Arabia and long-range transport from Iraq and Kuwait contributed the crustal components (Ca2+, HCO3–, K+, and Mg2+), which accounted for 36% of the total ionic FWD, whereas the Arabian Gulf was the primary source of the marine components (Na+ and Cl–). We found combining satellite observations and cluster analysis of backward air mass trajectories to be an effective new approach for assessing the source-receptor relationships for atmospheric air pollutants.

Application of Magnetic Susceptibility in Source Identification of Heavy Metal Pollution in Sediments of Chennai Coast, Tamilnadu Using Multivariate Statistical Analysis

Application of Magnetic Susceptibility in Source Identification of Heavy Metal Pollution in Sediments of Chennai Coast, Tamilnadu Using Multivariate Statistical Analysis

Fine dust atmospheric pollution from the objects of infill construction

Air pollution emissions are released from both natural and anthropogenic sources. During the environment pollution researching and monitoring the special attention should be paid to the construction operations, since during the construction processes many pollutants are released, especially fine dust particles, which are harmful to the health of construction workers and the population living near the construction site. The construction of any object in urban terrain has bad influence not only on the nearby buildings and city infrastructure, but on the existing environment of urban areas. The identification of the important pollution sources that contribute to ambient concentrations of pollutants is essential for developing an effective air quality management plan during building construction. Particular attention should be paid to emissions of fine particles during technological processes of construction with a special degree of dust emission. Control and regulation of the dynamic state of dispersed systems released during technological construction processes using a number of protective measures will reduce emissions of pollutants into the air. The objects of this research were the construction site and residential buildings of a large residential complex «Ekaterininskiy» located in Rostov-on-Don. The obtained measurement data as a result of this type of environmental monitoring showed the level of atmospheric air pollution from the construction industry using the example of the construction of the residential complex “Ekaterininskiy” in Rostov-onDon. After analyzing the situation with dust pollution the protective measures were suggested.

Pollution Characterization, Source Identification, and Health Risks of Atmospheric-Particle-Bound Heavy Metals in PM10 and PM2.5 at Multiple Sites in an Emerging Megacity in the Central Region of China

ABSTRACT A total of 588 daily PM filters were collected at five sites in Zhengzhou, and the mass concentrations and sources of the elements were analyzed. The health risks and source regions of the particles and toxic elements were also estimated. The results indicated severe PM2.5 and PM10 pollution, especially at traffic sites. Additionally, the PM10-bound As far exceeded the Chinese standards. Although the total elemental levels were relatively low at the rural site, they were high at the GY site. High levels of crustal elements were also observed at the SSQ and HKG sites. Seasonal-variation analysis revealed that the crustal elements, more abundant in the PM10, occurred at high levels in spring; the combustion-source elements, more abundant in the PM2.5, caused significant pollution in winter; and the elemental concentrations were low in summer. The coefficients of divergence for the PM2.5 were slightly higher than those for the PM10. Vehicles, industry, coal combustion, oil fuel, dust, and biomass burning were important sources of the PM-bound elements. Although the ZM site was characterized by low traffic and high contributions from biomass burning and dust emission, the HKG site featured a high proportion of emissions from traffic sources, and the SSQ site was also highly affected by vehicular pollution. Whereas elements in the PM2.5 largely originated in combustion sources, those in the PM10 received greater contributions from dust sources. The levels of As and Ni posed intolerable carcinogenic risks (CR) and, along with that of Pb, also demonstrated significant non-CR risks. Children were more sensitive than adults to these risks, and the daily intake pathway demonstrated the highest CR and hazard index (HI) values. Obvious differences in the CR and HI values were detected between the various sites, suggesting the necessity of multiple-site studies for health risk assessment. Jiyuan, Jiaozuo, Xuchang, and Zhoukou; Pingdingshan and Nanyang; and Jiyuan, Jiaozuo, Xinxiang, Anyang, and Kaifeng were the main potential sources of PM2.5, PM10, and As, respectively.

Collaborative based pollution sources identification algorithm in water supply sensor networks

Collaborative based pollution sources identification algorithm in water supply sensor networks

Bayesian pollution source identification via an inverse physics model

The behavior of air pollution is governed by complex dynamics in which the air quality of a site is affected by the pollutants transported from neighboring locations via physical processes. To estimate the sources of observed pollution, it is crucial to take the atmospheric conditions into account. Traditional approaches to building empirical models use observations, but do not extensively incorporate physical knowledge. Failure to exploit such knowledge can be critically limiting, particularly in situations where near-real-time estimation of a pollution source is necessary. A Bayesian method is proposed to estimate the locations and relative contributions of pollution sources by incorporating both the physical knowledge of fluid dynamics and observed data. The proposed method uses a flexible approach to statistically utilize large-scale data from a numerical weather prediction model while integrating the dynamics of the physical processes into the model. This method is illustrated with a real wind data set.

Application of Nitrogen and Oxygen Isotopes for Source and Fate Identification of Nitrate Pollution in Surface Water: A Review

Nitrate pollution in surface water has become an environmental problem of global concern. The effective way for controlling the nitrate pollution of surface water is to identify the pollution source and reduce the input of nitrate. In recent decades, nitrogen (δ15N) and oxygen (δ18O) isotopes of nitrate has been used as an effective approach for identifying the source and fate of nitrate pollution in surface water. However, owing to the complexity of nitrate pollution source and the influence of isotopic fractionation, the application of this method has some limitations. In this work, we systematically discussed the fundamental principle of using nitrogen and oxygen isotopes to trace the nitrate source, the fate identification of nitrate, and the major testing techniques. Subsequently, the applications of nitrogen and oxygen isotopes for source identification of surface water were illustrated. However, there are still significant gaps in the application of the source identification and transformation mechanisms to nitrate and many research questions on these topics need to be addressed.

Geochemical profile and source identification of surface and groundwater pollution of District Chitral, Northern Pakistan

This study investigated the physicochemical parameters of the ground and surface water of district Chitral, northern Pakistan, with particular emphasis on the fate, source distribution and hydrogeochemistry. To better understand the ground and surface water hydrogeochemical profile, groundwater (n = 39) and surface water (n = 22) were collected from Asheriat and Drosh regions of the study area. The range of pH, Temperature, EC, TDS, TSS, Turbidity, Salinity, in the groundwater and surface water were ((6.3–7.6, 14.5–20.5 °C, 440–690 μS/cm, 255–438 mg/L, 2–21 mg/L, 3.9–15.7 NTU, and 0.1–0.5)) and ((6.4–7.7, 15.5–23 °C, 447–708 μS/cm, 277–445 mg/L, 5–76 mg/L, 4.7–19.3 NTU, 0.1–0.6)) respectively. Similarly the chemical parameters Cl, NO3, SO4, PO4, HCO3, Na, K, Ca and Mg were ((50–120, 17.5–75, 65–138, 11–45, 255–453, 19.5–157, 4–14, 25–140 and 16.5–51) mg/L and ((65–110, 15.5–53, 82.5–142.5, 4.5–24.5, 250–375, 23–128, 4.5–12.5, 37–140 and 15–42.5)) mg/L respectively. This study shows that EC, TSS, Turbidity, NO3, PO4, Ca and Mg exceeded the WHO's guideline values. The correlation coefficient (r) value of Na was significantly positive correlated with, HCO3 and K, and inversely related with NO3, Ca and Mg. The hydrogeochemistry of groundwater reveals more contamination than surface water because of the water rock interaction and minerals dissolution. Whereas, the surface water showed dilution due to surface recharge from river Chitral and surrounding tributaries. The groundwater and surface water shows similar cationic and anionic concentrations trend across the entire study area increased in the following order: Na > Ca > Mg > K and HCO3 > SO4 > Cl > NO3 > PO4 respectively. Chadha diagram revealed that the dominant water type of the area were NaHCO3, and CaHCO3 type. The PCA and Gibb's results of groundwater and surface water quality showed the influences of the weathering of rocks and dissolution of minerals.

Common flaws in the analysis of river sediments polluted by risk elements and how to avoid them: case study in the Ploučnice River system, Czech Republic

River channel sediments have been widely used to trace current and historical pollution in fluvial systems, although they are not the only media employed for that purpose. The current practice in such use of sediments from sampling strategy, sample pre-treatment and geochemical data processing frequently includes unsubstantiated and redundant steps (e.g., sieving by sub-millimetre meshes) and neglects certain relevant aspects (insufficient attention to what is actually sampled). The aim of this work was to improve that state and to remove redundant steps that make research more time-consuming and potentially introduce biases. The work presents data obtained in the identification of pollution sources in a small catchment with three tributaries (the Ploucnice River, the Czech Republic). The sediments were manually sampled in river channels in sites of recent accumulation of fine solids transported by the river. The samples were pulverised and analysed by X-ray fluorescence spectroscopy. The sediments are composed mostly of silt and sand with most chemical elements, including risk elements “diluted” by quartz and detritic organic matter, which are perfectly suited for geochemical normalization to correctly process compositional data. We avoided statistical tools based on Gaussian distribution, such as means and standard deviations, and instead used median-based statistics better fitting the known properties of geochemical datasets. Sediment sieving to sub-millimetre-size fractions was replaced by geochemical normalization best with Fe and possibly with Rb and Ti, with Al showing implausible performance. The normalization produces grain-size invariant compositional data. The performance of geochemical normalization and downstream variations of risk element concentrations (Pb and Zn) were tested by median-based criteria. Median smoothing of normalized risk element concentrations produced easily interpretable downstream variations of the pollution extent that were independent of grain-size effects and were robust towards the occasional presence of outliers. Channel sediments are suitable for fluvial pollution monitoring if biased routines are avoided. The paper can inspire specialists who plan to perform environmental monitoring and desire to simplify their work and produce robust and unbiased estimates of pollution sources in fluvial systems under anthropogenic pressure. A similar approach could be tested for monitoring of pollution by further elements and hydrophobic organic compounds, except for the need to choose another normalizing element or TOC.

Identification of Nitrate Pollution Sources Through Various Isotopic Methods: A Case Study of the Huixian Wetland

In recent years, the ecological environment of wetlands has deteriorated under the predatory exploitation of human beings. Nitrate pollution is a major problem faced by wetland ecosystems. In this paper, Huixian karst wetland was selected as a study area to identify the main factors leading to the increase of nitrate in wetland water. The main sources of nitrate pollution in the area were determined by means of 15N(NO3-) and 18O(NO3-) isotopes. The contribution rate of all types of pollution sources was determined with the SIAR model. To explore the relationship between the spatial distribution of the nitrate concentration and the groundwater runoff conditions, the 13CDIC isotopes were used to qualitatively characterize the runoff conditions of the groundwater. The results show that there are three main factors affecting the nitrate concentration in the study area:animal manure and domestic sewage, NO3- in chemical fertilizer, and soil nitrogen. The average contribution rates to nitrate pollution are 39.1%, 32.2%, and 28.5% respectively. The 13CDIC isotope data clarify that a light 13CDIC reflects better groundwater runoff conditions. The concentration of NO3- is generally lower, while the NO3- concentration corresponding to heavy 13CDIC is generally higher. The condition of the groundwater runoff to a certain extent also affects the distribution of the NO3- concentration.

The application of 0–1 mixed integer nonlinear programming optimization model based on a surrogate model to identify the groundwater pollution source

The optimization model is presently used for the identification of pollution sources and it is based on non-linear programming optimization. The decision variables in this model are continuous, resulting in a weak recognition of integer variables including pollution source location. In addition, as the number of pollution sources increase, so the calculated load increases exponentially and accuracy decreases. Compared with previous studies, this study makes a series of improvements by adopting a 0–1 mixed integer nonlinear programming optimization model to enable the simultaneous identification of both location (integer variable) and the release intensity (continuous variable) of the pollution source. One of the constraints in the optimization model is a simulation component which requires thousands of calls during the calculation process and therefore requires considerable computational load. To avoid this problem, the Kriging surrogate model is established in this study to reduce computational load, while at the same time ensuring the accuracy of the simulation results. The identification result is solved using a genetic algorithm (GA) and represents the real location of the pollution source, while release intensities are close to actual ones with small relative errors. The Kriging surrogate model is based on a 0–1 mixed integer nonlinear programming optimization model and can simultaneously identify both the location and the release intensity of the pollution source with a high degree of accuracy and by using short computational times.

Holistic approach for quantification and identification of pollutant sources of a river basin by analyzing the open drains using an advanced multivariate clustering.

Global scarcity of freshwater has been gearing towards an unsustainable river basin management and corresponding services to the humans. It needs a holistic approach, which exclusively focuses on effective river water quality monitoring and quantification and identification of pollutant sources, in order to address the issue of sustainability. These days, rivers are heavily contaminated due to the presence of organic and metallic pollutants released from several anthropogenic sources, such as industrial effluents, domestic sewage, and agricultural runoff. It is astonishing to note that even in many developing countries, most of these contaminants are carried through open drains, which enter river premises without proper treatment. Such practice not only devastates riverine ecosystem but also gives rise to deadly diseases, such as minimata and cancer in humans. Considering these issues, the present study develops a novel approach towards simultaneous identification of major sources of pollution in the rivers, along with critical pollutants and locations using an advanced hierarchical cluster and multivariate statistical analysis. A systematic approach has been developed by agglomerating both R-mode and Q-mode analysis, which develops monoplots, two-dimensional biplots, rotated component matrices, and dendrograms (using "SPSS" and "Analyse It" software) to reveal relationships among various quality parameters to identify the pollutant sources along with clustering of critical sampling sites and pollutants. A case study of the Ganges River Basin of India has been considered to demonstrate the efficacy and usefulness of the model by analyzing 85 open drains. Both organic and metallic pollutants are analyzed simultaneously as well as separately to get a holistic understanding of all the relationships and to broaden the perspective of water characterization. Results provide a comprehensive guidance to the policy makers and water managers to optimize corrective efforts, minimize further damage, and improve the water quality condition to ensure sustainable development of the river basin.

Research Progress on the Sources of Inorganic Nitrogen Pollution in Groundwater and Identification Methods

The identification of the main inorganic nitrogen (MIN, referring to NH4+-N, NO3--N, and NO2--N) pollution sources in groundwater is of great significance to its control and repair. A review of the MIN sources in groundwater and the main identification methods was conducted. The main sources of MIN pollution in groundwater (atmospheric nitrogen deposition, soil natural organic nitrogen mineralization, nitrogen from streams, and nitrogen emission from human activity), and its distribution in China were expounded. The common methods for tracing MIN sources include hydrochemical analysis, statistical estimation, regional nitrogen balance evaluation, stable isotopes tracer, and new types of tracers. Because of the variety of nitrogen sources and the complexity of the MIN pollution formation mechanism, the single identification methods shared limitations in application, whereas more comprehensive ones, especially the stable isotope tracer integrated with other methods, were mainstream. Furthermore, future research prospects, including the development of new types of tracing methods, the optimization of quantification methods, the integration of research on pollution source identification, transformation mechanism, groundwater recharge and discharge condition, and groundwater-surface water conversion, have been put forward.

Fecal source identification using random forest

BackgroundClostridiales and Bacteroidales are uniquely adapted to the gut environment and have co-evolved with their hosts resulting in convergent microbiome patterns within mammalian species. As a result, members of Clostridiales and Bacteroidales are particularly suitable for identifying sources of fecal contamination in environmental samples. However, a comprehensive evaluation of their predictive power and development of computational approaches is lacking. Given the global public health concern for waterborne disease, accurate identification of fecal pollution sources is essential for effective risk assessment and management. Here, we use random forest algorithm and 16S rRNA gene amplicon sequences assigned to Clostridiales and Bacteroidales to identify common fecal pollution sources. We benchmarked the accuracy, consistency, and sensitivity of our classification approach using fecal, environmental, and artificial in silico generated samples.ResultsClostridiales and Bacteroidales classifiers were composed mainly of sequences that displayed differential distributions (host-preferred) among sewage, cow, deer, pig, cat, and dog sources. Each classifier correctly identified human and individual animal sources in approximately 90% of the fecal and environmental samples tested. Misclassifications resulted mostly from false-positive identification of cat and dog fecal signatures in host animals not used to build the classifiers, suggesting characterization of additional animals would improve accuracy. Random forest predictions were highly reproducible, reflecting the consistency of the bacterial signatures within each of the animal and sewage sources. Using in silico generated samples, we could detect fecal bacterial signatures when the source dataset accounted for as little as ~ 0.5% of the assemblage, with ~ 0.04% of the sequences matching the classifiers. Finally, we developed a proxy to estimate proportions among sources, which allowed us to determine which sources contribute the most to observed fecal pollution.ConclusionRandom forest classification with 16S rRNA gene amplicons offers a rapid, sensitive, and accurate solution for identifying host microbial signatures to detect human and animal fecal contamination in environmental samples.

Inverse uncertainty characteristics of pollution source identification for river chemical spill incidents by stochastic analysis

Identifying source information after river chemical spill occurrences is critical for emergency responses. However, the inverse uncertainty characteristics of this kind of pollution source inversion problem have not yet been clearly elucidated. To fill this gap, stochastic analysis approaches, including a regional sensitivity analysis method, identifiability plot and perturbation methods, were employed to conduct an empirical investigation on generic inverse uncertainty characteristics under a well-accepted uncertainty analysis framework. Case studies based on field tracer experiments and synthetic numerical tracer experiments revealed several new rules. For example, the release load can be most easily inverted, and the source location is responsible for the largest uncertainty among the source parameters. The diffusion and convection processes are more sensitive than the dilution and pollutant attenuation processes to the optimization of objective functions in terms of structural uncertainty. The differences among the different objective functions are smaller for instantaneous release than for continuous release cases. Small monitoring errors affect the inversion results only slightly, which can be ignored in practice. Interestingly, the estimated values of the release location and time negatively deviate from the real values, and the extent is positively correlated with the relative size of the mixing zone to the objective river reach. These new findings improve decision making in emergency responses to sudden water pollution and guide the monitoring network design.

Volatile Organic Compounds in Homes in Shanghai, China: Identification of Pollutant Sources and Impact of Air Purifier Use on Pollutant Levels

Although Chinese residents spend the majority of their time indoors, pollutants in the indoor environment have not been studied as extensively as their outdoor counterparts. This is especially true of volatile organic compounds (VOCs), many of which are associated with adverse health outcomes and have indoor sources. For this study, 20 homes with asthmatic children were recruited from the suburbs of Shanghai, China. This study explored pollutants and the impacts of air filtration on pollutant levels. Air purifiers were installed twice, for two-week periods, in the child’s bedroom – once with all levels of filtration (pre-filter, HEPA filter, carbon filter) and again with only a coarse pre-filter. During both periods, indoor and outdoor samples were collected simultaneously on Tenax and DNPH tubes in the early morning using pump packages developed at Duke University. Of the 900+ compounds detected, 117 were detected in 10 or more samples. Total VOCs were higher indoors than outdoors in most homes under both conditions (median values [in mg m-3] - sham, in/out: 235/59; true, in/out: 119 /77). There was a significant reduction in indoor-outdoor ratios during true compared to sham filtration indicating a potential benefit of filtration (median reduction: 36%, range: -224%, 96%). Non-negative matrix factorization was used to identify groupings of compounds (features) illustrative of sources. Of the features identified, two indicate outdoor sources with certain compounds detected in most outdoor samples but seldom detected indoors. The other three features point to indoor sources. Preliminary analyses indicate compounds detected predominantly indoors may relate to cleaning or personal care products, although analysis is ongoing. This work provides new insight to potentially harmful compounds in Chinese homes, as most previous studies have focused on assessment of total VOCs or select, well-characterized VOCs including BTEX and formaldehyde.

Temporal variations, sources and tracer significance of Polonium-210 in the metropolitan atmosphere of Beijing, China

In the present study, Polonium-210 (210Po) is studied in TSP and coal combustion samples for its potential application in tracing air pollutants. A significant seasonal variation of atmospheric 210Po in Beijing was observed: winter > autumn > summer > spring. During the combustion of coal, 210Po was highly enriched in fly ash but was depleted in bottom ash. Using selenium (Se) as an indicator of coal combustion, significantly positive correlation between 210Po and Se in TSP samples (r2 = 0.705) revealed that coal combustion was a primary source of atmospheric 210Po in Beijing. The mean emission factor of 210Po from industry sector was found to be the highest (1.15 Bq kg−1), followed by the residential (0.308 Bq kg−1) and coal-fired power plant (0.0776 Bq kg−1) sectors. The annual emission inventories of 210Po from three coal consumer sectors (residential, coal-fired power plant, and industry) in 2016 in Beijing were estimated to be 1.49, 0.0729, and 1.36 GBq, respectively. The ratio of radioactive 210Po and stable element Se (210Po/Se) was creatively used as a combined indicator or fingerprint of coal combustion emission, which will contribute to the development of radioisotope tracer technology based on nuclide fingerprint database in the source identification of air pollutants.