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Volume 2, Number 11, November 2013 (Serial Number 23)

Journal of Environmental

Science and Engineering B

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Journal of Environmental Science and Engineering B

Volume 2, Number 11, November 2013 (Serial Number 23)

Contents Environmental Monitor

621 Ecological Monitoring Perspectives from Biotesting of Surface Waters: A Study of Pavlovsk Reservoir (Bashkortostan, Russia) Dmitry Seifert, Yagafar Abdrashitov, Elena Bakhonina and Inna Ovsyannikova

Environmental Management

629 Temporal Changes in Transboundary Air Pollutants in Bottom Sediments of Lakes in East Asia Shintaro Murai, Ryosuke Sato, Masaki Hashimoto, Akiko Murakami-Kitase, Hideo Yamazaki, Shusaku Yoshikawa, Ju-Yong Kim and Kazuo Kamura

640 Cave Environmental Changes: The Possibilities of Assessment Elena Trofimova

643 Simultaneous Measurements of Rainfall Intensity, Low Energy Neutrons and Gamma Radiation in So Jos Dos Campos, SP, Brazil Marcelo Pego Gomes, Incio Malmonge Martin, Mauro Angelo Alves, Bogos Nubar Sismanoglu, Marco Antonio S. Ferro, Marcos Pinto and Flvio Antonio

Agricultural Environment

648 Rice Growth and Yield in Humid Forest of Cte dIvoire as Affected by Different Sources of Phosphates in Ferralsol Fatogoma Sorho, Brahima Kon, Jean Baptiste Ettien, Migninna Joachim Traor and Edja Fulgence Akassimadou

Environment & Energy

657 Model of Wetland Carbon Sequestration in the Venetian Lagoon, Italy Mauro Doimi, Angelo Ferrari, Daniele DalMolin and Italo Gardan

Environmental Chemistry

672 Overview of Simple Test for Determination of Surfactants by Adhesion Method Minori Kamaya

Journal of Environmental Science and Engineering B 2 (2013) 621-628 Formerly part of Journal of Environmental Science and Engineering, ISSN 1934-8932

Ecological Monitoring Perspectives from Biotesting of Surface Waters: A Study of Pavlovsk Reservoir

(Bashkortostan, Russia)

Dmitry Seifert1, 2, Yagafar Abdrashitov2, Elena Bakhonina1 and Inna Ovsyannikova1 1. Department of Ecology and Environmental Management, Branch of Ufa State Petroleum Technological University, Sterlitamak

453118, Russia.

2. Department of Chemistry, Branch of Bashkir State University, Sterlitamak 453103, Russia. Received: September 23, 2013 / Accepted: October 28, 2013 / Published: November 20, 2013. Abstract: The cost of environmental analysis is becoming astronomically high at the global scale. One of the major trends in the respective research activities is the development of biotesting methods. Such methods, in addition to ecotoxicology, are highly demanded for environmental monitoring and ecological standardization. The development of biotesting in toxicology, however, is limited to the battery of tests paradigm, while environmental monitoring and ecological standardization are based on the uniformity of measurements paradigm. A reference bioindicator is proposed to harmonize these approaches. A reference bioindicator serves for comparison of data obtained by different bioindicators. This method was approved for the state environmental control. Application of reference bioindicators makes analytical procedure substantially cheaper. It requires, however, thorough calibration in relation to specific environmental factors (such as temperature, photoperiod etc.) as well as to specific active agents and their combinations. This problem can be solved with the start-up of calibrating analytical centers and long-term study of the effects of spatiotemporal environmental factors in specific areas and for specific reference objects. This paper demonstrates long-term study results for the surface waters of Pavlovsk reservoir (Bashkortostan, Russia). Key words: Biotesting, phyto-testing, garden cress, eutrophication and toxication of water bodies, environmental monitoring, environmental control.

1. Introduction

The cost of the environmental analysis is becoming astronomically high at the global scale. This cost includes expenditures for laboratory equipment purchase and carrying out of analysis. On the other hand, we are facing constant expanding of the list of anthropogenic toxic substances polluting our environment and therefore need less expensive and integral methods for the estimation of environmental conditions [1]. Such methods are supposed to be based on the parameters being the combination of various factors, such as the results of physicochemical analysis, natural conditions and specific ecological

Corresponding author: Dmitry Seifert, Dr. Sci., main research field: ecotoxicology. E-mail: [email protected].

indicators considering diversity of species with the domination of specific ones, etc..

One of the major trends in the respective research activities is the development of adequate biotesting. Such methods, in addition to ecotoxicology, are highly demanded for environmental monitoring and ecological standardization. The development of biotesting in toxicology, however, is limited to the battery of tests paradigm, while environmental monitoring and ecological standardization are based on the uniformity of measurements paradigm [2]. Biotesting in the Russian Federation was mostly developed by the introduction of new experimental methods and not by the analysis of accumulated empirical materials. Such an approach reduces

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predicting ability of scientific information and complicates comparison of experimental data from different methods. On the one hand, there is a large number of testing microorganisms. On the other hand, they demonstrate different responses to environmental changes.

Environmental monitoring of surface waters requires spatiotemporal dynamics of parameters under analysis. It is necessary to find the optimal ratio among large number of different analyses important for maintaining high quality of life and their cost-effectiveness. The obtained solution was the development of chemical analysis tests including biological testing methods (biotesting) [3-5]. In addition, transformations occurring in the environment lead, to the synthesis of new substances which can be even more toxic than their precursors. Such substances are, for example, methylmercury, detergents with heavy metals in their structure, pesticides, etc..

The first step to the interpretation of ecotoxicity research results is the development of algorithm for their analysis. The example is the method guidance and recommendations for WET (whole effluent toxicity) Testing developed by the US EPA (Environmental Protection Agency) [6]. This method guidance offers the description of analyzed parameter as a dynamic parameter of toxicity for various test objects and degrees of dilution. Our research was dedicated to the development of a biotesting technique capable of harmonizing with the above US method. The minimal difference of these methods is that the dilution percentage is used in its American variant while we used the dilution ratio.

We have developed the procedure for toxicity testing of drinking, ground and waste waters as well as solutions of chemicals by germinating ability, average length and average dry weight of garden cress (Lepidium Sativum) sprouts, federal nature protection documents [7]. This method was approved for the state environmental control [7].

Garden cress (Lepidium sativum) is one of the most popular test objects for biotesting of water, bed sediments, soils, natural and anthropogenic substrates and radiation as well as effects of synthesized chemicals and their mixtures [8]. This procedure allows quantitative determination of eutrophication degree (eutrophication index) and the analysis of the interaction of eutrophication and toxication processes [9, 10].

Our procedure has three modifications: First modification is designed for the evaluation of

the most important parameters. It uses growth chambers with controlled temperature and photoperiod;

Second method is based on a room-temperature analysis. The disadvantage of this method is lower accuracy of measurements, while the advantage over the first modification is faster analysis;

Third modification offers analysis in ambient conditions (such as at terraces or in unheated premises) during the vegetation period. This modification does not include determination of the average dry weight (Fig. 1).

This procedure is based on the ecological diagnostics methods and tested for the following objects of natural or anthropogenic origin: natural and waste waters, solutions of various chemicals or pharmaceutical formulations in the environment, tobacco, tobacco ash, etc. [11-15].

The experimental objects studied in natural conditions were the surface waters of the Pavlovsk reservoir headwater. It is possible in principle to track the conditions of surface water in space and time [10, 15].

This procedure proved to be also applicable to the quantitative characterization of eutrophication with the consideration of simultaneous occurrence of eutrophication and toxication processes.

Construction of river dams is considered to be mechanical pollution of the environment according to the present-day definitions. Creation of reservoirs


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Fig. 1 The example of modification for ambient conditions.

results in lowering or stopping of rivers and massive accumulating of biogenic and organic substances in the backwater zone with large flooded areas. River beds also enrich water with biogenic and organic substances from soil leaching and decomposition of flooded plants. Temperature and light conditions change and increase flooding lowers oxygen concentration in water [16].

2. Materials and Methods

Pavlovsk reservoir is formed by the Pavlovsk hydropower station dam at the river Ufa, Republic of Bashkortostan, Russia. Pavlovsk reservoir was flooded in 1959-1961. The total area is 120 km2, the maximum width is 2 km, the volume is 1.41 km3, the length is 150 km, and the average depth is 11.5 m. The reservoir is used for the seasonal control of water flow, water transportation, rafting of timber, water supply; it also contributes to the development of energy industry. The main sources of anthropogenic substances in the reservoir are timber, compounds of

agricultural origin (mineral fertilizers, effluents from cattle farms, pesticides and toxic chemicals), municipal and industrial wastes from the regions of Chelyabinsk, Sverdlovsk and the Republic of Bashkortostan.

Anthropogenic influence on water bodies is conditionally subdivided into eutrophication and toxication [17]. The eutrophication effect is usually caused by biogenic elements and organic substances usually considered to be harmless. They stimulate growth of various microorganisms. The result may be the disruption of the ecological balance and secondary pollution by various metabolites. Ecosystems face simultaneous pressure from both toxic and eutrophicating factors. The majority of measurements, however, consider toxic effects only. Eutrophication can be evaluated through changed productivity of water systems, various characteristics of phytoplankton and the change of nitrogen and phosphorus concentrations [18-20]. It is necessary to note that eutrophication parameter is included into regulatory documentation without respective


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procedures, the methods for quantitative evaluation of the eutrophication level and consideration of the joint effect of eutrophication and toxicity [2].

The total amount of only three biogenic elements dumped into Pavlovsk reservoir over a year is about 17,400 tons (9,200 tons of nitrogen, 2,500 tons of phosphorus and 7,700 tons of potassium) [21]. It creates a problem of removing the faults of anthropogenic activities. A solution requires achievement of three interrelated sub-objective to measure the degree of object degradation by various parameters in the process of environmental monitoring; to plan the scale and the duration of activities required for the stabilization of an object; and to carry out all the planned activities at the proper time.

Long-term biotesting in the field conditions faces certain limitations such as weather and photoperiod differences. The purpose of the research was the estimation of the surface water conditions at the headwater of Pavlovsk reservoir (Fig. 2). The scale of ecological effects was studied at the SOLUNI research and industrial test park in 2011-2013. Four reference cross-sections were chosen for taking water samples. The samples were taken in June 2011, every month of the vegetation period in 2012, in June 2013 and August 2013.

The field modification of the procedure does not include the measurement of the average dry weight of sprouts.

It was demonstrated that the concentration of biogens reduces over a vegetation period in water reservoirs located in the studied natural zone [22].

3. Results

The following eutrophication index values were obtained in 2011 (Fig. 3). The research results reveal both yearly dynamics of the water eutrophication index and its changes during the vegetation period. In June 2011, the highest index values were observed at Aviator biological treatment plant effluent discharge site (cross-section 1), while the minimal

Fig. 2 Reference cross-sections at the headwater of Pavlovsk reservoir: 1-Aviator biological treatment plant discharge site; 2-the recreation cottage area; 3-SOLUNI biological treatment plant discharge site; 4-in quiet reach.

Fig. 3 The water eutrophication index values (mm) at the reference cross-sections in June 2011.

were obtained in the area in front of the cross-section (cross-section 4), other values demonstrated no differences. In June 2012, index values were highest at the cross-section 2 and in front of the cross-section (cross-section 4). Cross-section 2 revealed the opposite correlation between the degree of effluent dilution and the average length of sprouts (r = -0.80) indicating sufficiently strong water eutrophication.


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This correlation was not reliable in the other periods (correlation factors were in the range between -0.15 and 0.02). Similar correlation was revealed earlier for the surface waters of the Belaya river in the Sterlitamak city area [8]. At the same time, the Aviator biological treatment plant effluent discharge site demonstrated no changes in the index values after

June 2011 due to the negative effect of effluents. Similar reliable but less negative effect of SOLUNI biological treatment plant was observed at the cross-section 3.

In 2012, there were short spring and early summer temperatures. These values were obtained again in June, July and August 2012 (Fig. 4).

June

July

August

Fig. 4 The water eutrophication index values (mm) at various sample collection sites in 2012.


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In July 2012, the lowest eutrophication index value was obtained for the cross-section in front of the recreation cottage area (Fig. 4). These values, however, were in total lower than in June 2012 and corresponded to the highest index values in June 2011.

In August 2012, the index values were reliably different for the cross-sections 3 and 4 (Fig. 4). The highest values corresponded to the ones in June 2012.

In June 2013, the index values were reliably different for the cross-sections 1 and 3 as well as for the cross-sections 3 and 4. The reliable occurrence of a toxic effect was observed for the cross-sections 1 (r = 0.64), while reliable eutrophication effect was detected for the cross-sections 1, 2 and 3 by the average length of sprouts (r = -0.68, r = -0.56, r = -0.73, respectively) (Fig. 5).

June

August

Fig. 5 The water eutrophication index values (mm) at various sample collection sites in 2013.


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Table 1 The matrix of correlation coefficients between eutrophication index values at the studied period (2011-2013). Reference cross-sections Cross-section 1 Cross-section 2 Cross-section 3 Cross-section 4 Cross-section 1 1.00 0.58 0.80 0.51 Cross-section 2 0.58 1.00 0.79 0.92 Cross-section 3 0.80 0.79 1.00 0.85 Cross-section 4 0.51 0.92 0.85 1.00

The reliable correlation coefficients are marked with bold type.

A reliable influence of toxicity on germinability (r = 0.68) was revealed in August 2013, while cross-section 3 demonstrated a long-term eutrophication effect (r = -0.54). Eutrophication effects were also revealed in the cross-sections 3 and 4 (r = -0.62 and r = -0.93 respectively) from the analysis of the average length of sprouts. The influence of toxicity on germinability was detected earlier only for the waste waters meant for biological treatment plants (before and after treatment).

Reliable differences of eutrophication index values were obtained for the cross-sections 1, 2 and 4 as well as for the cross-sections 3 and 4 in August 2013.

4. Discussion

It is very important to provide ecologically adequate interpretation of results. The lowest values of eutrophication index for the entire period of studies were observed in 2011. Such differences are probably caused by weather factors. These observations are confirmed by the simultaneous changes of eutrophication index values at various study periods (Table 1).

Due to different correlations between biotesting parameters and the dilution rate of affecting samples, it is possible to suppose that the index value is the resulting combination of the eutrophication level and the degree of water toxicity. In 2011, all the collected samples demonstrated neither suppression nor stimulation of the growth of seeds. In 2012, the eutrophication effect was observed at the reference cross-section only in July (cross-section 2). In 2013, the same effect was revealed at the reference cross-section in June. The stimulating effect was observed for the cross-sections 1 and 3 in June and for

the cross-sections 1 and 4 in August. Opposite germinability behavior was revealed in that year for the first time: the toxic effect for the cross-section 1 and the stimulating effect for the cross-sections 3.

The cross-section 2 is located upstream to the local discharge sources and therefore, is considered as the reference site demonstrating integral conditions of the surface waters at the reservoir region under study. The values of eutrophication index at the cross-section 2 change in a way similar to the behavior of the cross-section 4 (Table 1).

The Aviator base effluent discharge site is the most toxic of the local sources. SOLUNI biological treatment plant effluent site also contributes to the resulting negative outcome in certain periods. The most stable behavior of the studied area of Pavlovsk reservoir was observed in 2011, with the most unstable one in 2013. This research needs to be continued to draw a solid conclusion on increased eutrophication level at the studied area of Pavlovsk reservoir. Further research activities will involve the analysis of collected data and the contribution of weather factors and will result in the development of a model predicting eutrophication levels and toxication of water levels. Such a model will be easily applicable to the characterization of other water reservoirs in the Republic of Bashkortostan, Russia.

References [1] N.G. Bulgakov, Environment Control as the Set of

Methods of Bioindication, Ecological Diagnostics and Standardization, Issues of Environment and Natural Resources: A Review, All-Union Institute of Scientific and Technical Information, 2003, pp. 33-70. (in Russian)

[2] D.V. Seifert, I.H. Bikbulatov, E.M. Malikov, O.R. Kadyrov, Environmental Quality Standards, Bashkir State University, Ufa, 2004. (in Russian)


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[3] Yu.A. Zolotov, Analytical chemistry: Development trends in 1950-1990, Zhurnal Analiticheskoy Khimii 48 (7) (1993) 1116-1126. (in Russian)

[4] M.I. Evgeniev, Testing methods and ecology, Soros Education Journal 11 (1999) 29-34. (in Russian)

[5] T.N. Shekhovtseva, Biological models of analysis, Soros Education Journal 11 (2000) 17-21. (in Russian)

[6] Method Guidance and Recommendations for WET (Whole Effluent Toxicity) Testing, EPA 821-B-00-004, Washington, DC, 2000.

[7] Toxicity Testing of Drinking, Ground and Waste Waters as Well as Solutions of Chemicals by Germinating Ability, Average Length and Average Dry Weight of Garden Cress (Lepidium Sativum) Sprouts, Federal Nature Protection Documents 14.1:2:4.19-2013, Moscow, Russia, 2013. (in Russian)

[8] D.V. Seifert, The use of garden cress for testing the toxicity of natural and waste waters of sterlitamak industrial cluster, Bashkirskiy Ecologicheskiy Vestnik 2 (2010) 39-50. (in Russian)

[9] D.V. Seifert, I.V. Ovsyannikova, D.I. Zakiryanov, L.M. Gamerova, V.A. Efremova, M.I. Izvestieva, Yearly and seasonal dynamics of eutrophication parameters at the headwater sites of Pavlovsk reservoir, biodiagnostics of natural and natural-anthropogenic systems, in: Xth Russian Scientific and Technical Conference with Invited International Participants, Loban, Kirov, Russia, 2012. (in Russian)

[10] D.V. Seifert, E.F. Gareeva, D.T. Gabbasova, Phytotesting for ecological monitoring of the effect of biological waste water treatment facilities on the quality of reservoir water, Ecologicheskiy Vestnik Rossii 11 (2011) 34-39. (in Russian)

[11] D.V. Seifert, O.A. Knyazeva, I.G. Konkina, F.R. Oparina, F.R. Tukumbetova, A.I. Urazaeva, The evaluation of phytotoxicity of gluconates and chlorides formed by D-elements with garden cress, Bashkirskiy Khimicheskiy Zhurnal 19 (4) (2012) 20-23. (in Russian)

[12] D.V. Seifert, V.B. Barachnina, The evaluation of the toxic influence of pharmaceutical products on the environment, Archives of Waste Management and Environmental Protection 14 (1) (2012) 11-18.

[13] D.V. Seifert, E.G. Stepanov, V.A. Vasilieva, N.M.

Kademskaya, The evaluation of phytotoxicity of tobacco and tobacco ash, Ecologicheskiy Vestnik Rossii 3 (2013) 64-69. (in Russian)

[14] D.V. Seifert, I.V. Ovsyannikova, M.V. Makarova, The quality of water in centralized drinking water supply systems: To drink or not to drink, Ecologicheskiy Vestnik Rossii 7 (2013) 30-33. (in Russian)

[15] D.V. Seifert, I.V. Ovsyannikova, D.I. Zakiryanov, L.M. Gamerova, R.R. Khairullina, E.I. Sakaeva, et al., Perspectives of ecological biotesting of surface waters: A Pavlovsk reservoir (Republic of Bashkortostan, Russia) example, in: Proceedings of Russian Scientific Practicum Application of Biotesting Methods to Environmental Monitoring and Control, Ufa State Petroleum Technological University, Ufa, Russia, 2013. (in Russian)

[16] A.O. Poleva, A comprehensive assessment of the Pavlovsk reservoir ecosystem, Ph.D. Thesis, BSU, Ufa, Russia, 2009. (in Russian)

[17] O.V. Filenko, I.V. Mikheeva, Foundations of Water Toxicology, Koloss, Moscow, 2007. (in Russian)

[18] V.H. Smith, Phytoplankton responses to eutrophication in inland waters, in: F. Akatsuka (Eds.), Introduction to Applied Phycology, SPB Academic Publishing bv, The Hague, The Netherlands, 1990, pp. 231-249.

[19] V.H. Smith, G.D. Tilman, J.C. Nekola, Eutrophication: Impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems, Environmental Pollution 100 (1999) 179-196.

[20] V.H. Smith, B.J. Samantha, W.H. Robert, Eutrophication of freshwater and marine ecosystems, Limnol, Oceanogr., 51 (1) 2 (2006) 351-355.

[21] R.F. Abdrakhmanov, Formation of chemical constitution of pavlovsk reservoir waters, Gidrokhimicheskie Materialy 111 (1994) 139-150. (in Russian)

[22] G.S. Rosenberg, I.A. Evlanov, V.A. Seleznev, A.K. Mineev, A.V. Selezneva, V.K. Shitikov, The experience of ecological standardization of anthropogenic influence in the water quality (the example of middle and lower volga river reservoir), in: The Problems of Ecological Standardization and Development of Reservoir Water Condition Monitoring System, T-vo Nauchnyh Izdaniy KMK, Moscow, 2011, pp. 5-29. (in Russian)

Journal of Environmental Science and Engineering B 2 (2013) 629-639 Formerly part of Journal of Environmental Science and Engineering, ISSN 1934-8932

Temporal Changes in Transboundary Air Pollutants in Bottom Sediments of Lakes in East Asia

Shintaro Murai1, Ryosuke Sato2, Masaki Hashimoto1, Akiko Murakami-Kitase1, Hideo Yamazaki3, Shusaku Yoshikawa4, Ju-Yong Kim5 and Kazuo Kamura1 1. Faculty of Science and Engineering, Waseda University, Tokyo 169 8555, Japan

2. Mitsubishi Materials Corporation, Fukushima 971 8101, Japan

3. Faculty of Science and Engineering, Kinki University, Osaka 577 8502, Japan

4. Graduate School of Science, Osaka Sity University, Osaka 558 8585, Japan

5. Korean Institute of Geoscience and Mineral Resources, Gwahangro, Yuseong-gu, Daejeon Metropolitan City, Korea

Received: September 30, 2013 / Accepted: October 18, 2013 / Published: November 20, 2013. Abstract: To clarify temporal changes in the transboundary pollution, we analyzed SCPs (spheroidal carbonaceous particles) in bottom sediments of lakes in China, South Korea and Japan. SCPs provide an unambiguous record of anthropogenic atmospheric pollution in bottom sediments. Recently deposited SCPs in air and unmelted snow on Mt. Fuji were also characterized. The concentrarion and characteristics of SCPs reflected the environmental and industrial history of the area. Evidence of transboundary air pollution was observed after the 1980s in Yashagaike and Kotaniike ponds in Japan, which are located on the coast of the Sea of Japan. The concentration suggests that the pollutants originated from continental Asia, particularly after the 1980s. The chemical composition of the SCPs allowed the source of emissions to be identified. Chinese SCPs were found at Yashagaike and Kotaniike ponds in Japan, and at Songjiho and Urimji lakes in Korea. The size distributions showed that small SCPs are transported over long distances. On the summit of Mt. Fuji, SCPs transported by the prevailing westerlies from China were found in addition to SCPs emitted in Japan. Key words: Bottom sediments, transboundary air pollution, SCPs.

1. Introduction

The economy and industries of East Asian countries are growing rapidly. According to the National Bureau of Statics of China, Chinese GDP overtook Japanese GDP in 2010, and its annual rate of economic growth is greater than 7% [1]. Energy consumption in China is also increasing and the June 2011 BP Statistical Review of World Energy reported that China became the largest energy consumer in the world in 2010. Asias total primary energy consumption accounts for 38.1% of global energy consumption [2].

Consequently, the amount of air pollutants emitted

Corresponding author: Kazuo Kamura, Ph.D., professor,

main research field: environmental geology. E-mail: [email protected].

from factories and power stations is increasing, and heavy air pollution is spread over a wide area. In Japan, the rapid economic growth starting in the 1950s created serious air pollution; however, environmental regulations and pollution control technology have reduced the problem. In contrast, air pollution in China is still a major problem. The prevailing westerlies are expected to carry pollutants originating in China and thus cause transboundary air pollution.

There are periodic observation systems for transboundary pollution in East Asia, such as the Acid Deposition Monitoring Network in East Asia, in order to monitor the effect of air pollutants. However, data are limited on pollution after these observation systems were introduced. The bottom sediments of lakes provide a record of transboundary pollution. In

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lakes that are connected to few rivers, pollutants are deposited from the atmosphere and form temporal layers on the bottom. Therefore, pollutants in bottom sediments provide a record of anthropogenic pollution.

In this study, we focused on SCPs (spheroidal carbonaceous particles), which are produced by high-temperature combustion of fossil fuels and have no natural sources. Accordingly, the concentration of SCPs provides a record of the industrial history of the surrounding region [3]. The surface morphology and chemical composition of SCPs depend on the type of fuel, thus allowing the emission source to be identified.

Bottom sediment cores were collected in China, South Korea and Japan. The temporal changes in air pollution and the characteristics of the SCPs were determined from each sample. Air samples and unmelted snow samples were also collected at the top of Mt. Fuji, to analyze recent pollutants for comparison. Based on these results, we discuss the effect of transboundary air pollution in East Asia.

2. Methods

2.1 Research Materials

SCPs are anthropogenic porous spheroids mainly composed of elemental carbon, and generally known as fly ash. They are black and glossy when viewed under a microscope, and vary in size, from 3 m to 200 m. Most SCPs are deposited 70-100 km from the emission source, although some are transported thousands of kilometers by wind [4]. Particles usually smaller than 10 m are observed further from the emission source. Although SCPs are physically fragile, they are resistant to chemical attack, which allows them to be preserved well in the bottom sediments of lakes.

The surface morphology and chemical composition of SCPs depends on the fuel type. By analyzing each particle, the fuel and emission source can be identified. Particles produced from coal have rough or smooth

morphologies and are rich in Si and Al. Particles originating from oil have an irregular morphology and are rich in S.

2.2 Sampling Points

Eight bottom sediment cores were collected in China, South Korea and Japan. The sampling points were XIA (Xianshan Shuiku) and CHA (Chang Dang Hu) in China; SON (Songjiho) and UIR (Urimji) in South Korea; and KGZ (Karagamizeki), TSK (Tarusakaike), YSH (Yashagaike), and KOT (Kotaniike) in Japan (Fig. 1 and Table 1).

The samples from XIA and CHA were collected by diving in the lake and driving an acrylic pipe (diameter 7 cm) into the bottom sediment. For the other samples, a gravity core sampler (diameter 4 cm) was used to collect bottom core samples. The core was cut into 2-cm-thick slices and the samples were mainly composed of argillaceous minerals. The water content of each core sample increased monotonically from the deepest sample to the surface. One example of water content is showed in Fig. 2. Because there were no vertical transitions of color and grain size in the cores, we assumed that the sediment samples were deposited chronologically, and were preserved in an undisturbed state while on the lake bottom.

Air samples and unmelted snow samples were also collected on the summit of FUS (Mt. Fuji). Air samples were collected on filters with a low volume

Fig. 1 Location of sediment core samples and major cities in East Asia.

Beijing

Seoul SON

UIR

25

115 120 125 130 135 140

30

35

40

45

Shanghai

YSHKOT

CHA

Nanjing

XIA

TSK

KGZ

Tokyo

Pacific

OceanEast China

Sea

Sea of

Japan

FUS


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Table 1 Basic data of sampling points: sediment samples, unmelted snow and air samples.

XIA CHA SON UIR KGZ TSK YSH KOT

Longtitude/latitude 3629'44.2" 11924'37.9" 3136'46.8" 11931'0.6"

3820'18.7" 12830'34.1"

3710'17.9" 12812'39.2"

3530'8.7" 13330'18.6"

3500'6.7" 13637'8.5"

3567'27.8" 13629'00.7"

3530'8.7" 13330'18.6"

Water area (km) 88 87 0.57 0.12 0.002 0.001 0.004 0.01

Depth (m) 11 1 4 6 2 2 7 4

Length of core (cm) 57 25 60 40 32 80 53 40

Sampled date Sep. 2008 Sep. 2008 Aug. 2010 Aug. 2010 May 2011 Aug. 2009 Aug. 2011 Aug. 2012 Distance from nearest industry area (km) 90 120 180 120 4 5 80 150

FUS Sampled elevation 3,776 m Longtitude/latitude 3521'26.540'', 13843'49.772'' Sample dperiod (unmelted snow) June 2011, Feb. 2013 Sampled period (air sample) July and Aug. 2012, July and Aug. 2013

Fig. 2 Vertical profile of the water content ratio in the

YSH cores.

air sampler, at a flow velocity of 20 L/min for about 72 h. The unmelted snow samples were collected by scooping the top 20-cm-thick layer of snow. Samples were melted at room temperature and filtered with a 0.8 m membrane filter. The residue was analyzed.

2.3 Radioactive Dating

210Pb and 137Cs dating methods were used to date the sample slices [5]. Because 137Cs is anthropogenic, its concentration indicates various events. For example, the maximum 137Cs concentration indicates the atmospheric nuclear tests conducted in 1963. The deposition rates and sedimentation dates can be calculated using the radioactive half-life of 210Pb (22.3

years) and the profile of 137Cs in the core. The samples (~10 g) were placed in a lead block of

10 cm thickness for background radiation shielding, and the block was placed in a coaxial low-energy photon spectrometer (ORTEC, LO-AX/30P) and connected to a pulse height analyzer (NAIG, 4096) for about 200,000 s. RZMCA-ANA for Win by Laboratory Equipment Co. was used to analyze the -ray spectra.

2.4 SCPs Measurement Methods

We analyzed the concentration, particle size distribution, surface morphology, and chemical composition of the SCPs, based on the methods published by Yasuhara and Yamazaki [6]. Samples were dried at 60 C for 72 h. To extract the SCPs, unwanted organic matter, silicates, and carbonates were removed by HNO3, HF and HCl, in order to make observation easier. Purified microspheres (1 mL) were used to calculate the number of SCPs per gram of dry sediment. A suspension of the treated sample was then evaporated on two slide glasses. One was used to count the number of SCPs under an optical microscope at 200 magnification. The other slide was used to observe the morphology and chemical composition of the SCPs by scanning electron microscopy (SEM; HITACHI 3000-S) and energy-dispersive X-ray spectroscopy (EDS; EDAX

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Genesis). The concentrations of Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu and Zn were analyzed by EDS with a beam current of 100 m and an accelerating voltage of 25 kV for 50 s. The surface morphology was classified as rough, smooth, irregular or unclassified.

3. Results

3.1 SCP Surface Morphology and Chemical Composition

Content of the morphology is shown in Fig. 3. Unclassified SCPs are not shown. The chemical compositions are shown in Table 2.

(1) China: In the XIA and CHA samples, 90% of the particles were rough or smooth, indicating they came from coal. There was no oil SCPs in the XIA samples. Si was predominant in 80% of the particles and the average Si content of the XIA and CHA particles was 49 wt% and 53 wt%, respectively.

(2) South Korea: More than 80% of the particles in the SON and UIR samples were rough or smooth SCPs from coal, and 20% of the SCPs in the UIR samples were from oil, which was high, compared with the other sampling locations. The SCPs at these

two locations mainly contained Si, with an average Si content of 43 wt% and 33 wt%, respectively.

(3) Japan: At TSK and KGZ, the amount of S was higher than at the other sampling points, which is the characteristic of oil SCPs. About 10% of the SCPs had irregular morphology indicating oil SCPs. However, at YSH and KOT, the morphology of the SCPs was almost all rough or smooth. They were mainly composed of Si, and the average Si content at YSH and KOT was 42 wt% and 28 wt%, respectively. Most of the SCPs at FUS were rough or smooth. About 50% of the particles were rich in S and the other half were rich in Si. The average Si content was 24 wt% and the S content was 29 wt%.

Fig. 3 Content of SCPs with each morphology type.

Table 2 Average chemical composition of SCPs.

XIA (wt%) n = 49 CHA (wt%)n = 43

SON (wt%) n = 39

UIR (wt%)n = 77

KGZ (wt%)n = 77

TSK (wt%)n = 51

YSH (wt%) n = 163

KOT (wt%) n = 184

FUS (wt%)n = 23

Na 10.93 10.45 10.33 8.62 10.24 10.84 9.20 8.60 5.25 Mg 4.82 3.67 3.13 8.37 4.86 4.68 6.31 7.33 4.06 Al 6.17 3.96 2.28 12.88 3.89 12.86 8.22 16.64 11.69 Si 48.85 53.17 42.84 33.11 39.68 25.79 41.78 27.97 23.96 P 0.95 1.18 1.23 0.60 2.22 3.16 1.21 0.99 1.80 S 5.40 6.42 19.39 21.05 22.82 23.51 17.77 16.33 28.57 Cl 3.98 1.75 5.23 2.74 3.81 6.17 3.23 4.73 2.01 K 1.40 1.15 0.69 0.69 1.09 2.25 1.81 1.23 2.33 Ca 7.98 8.62 6.21 6.88 6.82 5.80 6.67 8.99 4.50 Ti 5.58 7.16 6.86 3.84 0.62 1.35 0.63 1.25 1.21 V 0.13 0.11 0.12 0.07 0.49 0.42 0.35 0.54 1.13 Cr 0.18 0.12 0.07 0.02 0.25 0.13 0.20 0.37 0.77 Mn 0.16 0.08 0.12 0.02 0.21 0.17 0.21 0.45 0.65 Fe 0.80 0.44 0.21 0.18 0.64 1.04 0.53 1.47 5.65 Co 0.35 0.20 0.12 0.04 0.38 0.29 0.31 0.60 1.12 Ni 0.61 0.31 0.32 0.20 0.71 0.59 0.53 0.84 1.59 Cu 0.73 0.54 0.38 0.22 0.52 0.41 0.47 0.84 1.66 Zn 1.00 0.70 0.47 0.40 0.74 0.55 0.57 0.82 2.06

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3.2 SCPs Concentration

The SCP concentrations are shown in Fig. 4. Some of these results have already been published [7, 8].

(1) China: At XIA, SCPs first appear in 1970s. They exceed about 1,200 grains/g around 1990, and increase up to the most recent samples. The maximum concentration is about 5,000 grains/g in 2003. At CHA, SCPs first appear in 1910s. They show a sudden increase in the more recent samples. The maximum concentration is about 5,700 grains/g in 1990s.

(2) South Korea: At SON, SCPs first appear in 1850s and increase until 1980s. After reaching a maximum concentration in 1980s (3,400 grains/g), the concentration decreases. At UIR, SCPs first appear in 1940s. The maximum concentration of about 8,300 grains/g is reached in 1980. It also increases until 1980s, and then decreases gradually.

(3) Japan: At KGZ, the SCP concentration also increases to a maximum of about 92,200 grains/g in 1980. The concentration then decreases dramatically

and is 12,100 grains/g in 2000s. At YSH, SCPs first appear in 1870s. The concentration increases, and then shows a sudden increase from 1960s to the present. The maximum concentration at YSH reaches about 8,200 grains/g in 2000s. At KOT, the SCP concentration remained the same until 1940s (~1,500 grains/g). It then increases to a maximum of about 12,100 grains/g in 1990s. The concentrations in 1990s and 2010s are similar.

3.3 SCP Size Distribution

The authors examined the size distribution in the YSH, KOT, and FUS samples. At YSH and KOT, the samples deposited after 1950s were analyzed. This is because we were able to examine more than 10 particles for each sample, meaning that the data analysis is reliable. SCPs were categorized in 10 m intervals (Fig. 5).

At YSH and KOT, the numbers of small SCPs 10 m increase since 1974. For FUS, the percentage of small SCPs < 20 m was greater than 70%.

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(d) (e) (f) (g)

Fig. 4 Concentration of SCPs within lakes in China: (a) XIA, (b) CHA, (C) SON, (d) UIR, (e) KGZ, (f) YSH, (g) KOT.

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Fig. 5 Size distribution of sediments: (a) YSH, (b) KOT, (c) FUS.

4. Discussions

4.1 SCP Chemical Composition Diagrams

4.1.1 Ti/Si vs. S/Si Diagram A diagram was constructed from the ratio of Ti, Si

and S. The vertical axis is Ti/Si and the horizontal axis is S/Si. Details are provided in Ref. [9], which made it possible to distinguish the emission source with fine precision.

Japanese SCPs (KGZ and TSK) are on the bottom right of the diagram. In contrast, Chinese SCPs (XIA and CHA) are on the middle left of the diagram. These results are similar to those reported in the past [9]. South Korean SCPs appeared mainly on the top right of the diagram, which shows the characteristics of the particles for each country (Fig. 6). The amount of oil consumption in Japan and Korea is relatively high. For that reason, the S contents of the SCPs in these countries are high. On the contrary, coal is the main fuel in China, the Si and Al content is high. Moreover, Chinese fly ash is Ti-rich [10]. Therefore, Japanese and Korean SCPs are similar but distinct from Chinese SCPs. However, Korean SCPs vary too widely to be a reliable standard. Therefore, we focused on particle size. There is a strong correlation between particle size and distance traveled [11]. SCPs smaller than 20 m can be emitted from locations far away from where they are collected [12, 13]. Therefore, we separated the SON and UIR SCP into particles smaller than 20 m and those larger than 20 m, and plotted them on the diagram.

At SON, SCPs smaller than 20 m are mainly on the top left, whereas SCPs larger than 20 m are split

equally between the top left and top right. At UIR, the majority of SCPs smaller than 20 m are mainly on the middle left, whereas almost all SCPs larger than 20 m are plotted on the top right. Fig. 6b indicates that the majorities of SCPs smaller than 20 m at SON are from coal, and are likely to be Chinese in origin. Furthermore, the majority of SCPs (> 20 m) for UIR are from oil, and are presumed to be from South Korea.

Another diagram was constructed for YSH, KOT and FUS. For YSH and KOT, we divided the samples into sediments deposited before 1980 and after 1980. This is because previous work suggests that the effect of transboundary air pollution in East Asia is clear after 1980 [14]. The SCP concentration in Fig. 6 also shows a sudden increase in pollutants in East Asia since the 1980s. To focus on the relationship between the size of the particles and the transport distance, the authors categorized the SCPs into those smaller than 10 m, 10-20 m, and larger than 20 m in size. SCPs for FUS were divided into particles smaller than 20 m and those larger than 20 m.

On the diagram for SCPs emitted before 1980, a large proportion of the particles at YSH and KOT were similar to those for KGZ and TSK. These particles are assumed to be Japanese in origin. There was no major difference in particle size. On the diagram for SCPs emitted after 1980, the SCPs at YSH were divided evenly between the middle left and bottom right. There were more Chinese compared with the diagram for before 1980. As the particle size decreases, the particles are more likely to be Chinese. In contrast, large SCPs tend to be Japanese. This suggests a strong correlation between size and transport

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