Contamination Levels, Monthly Variations, and Predictions of Neonicotinoid Pesticides in Surface Waters of Gifu Prefecture in Japan

Environmental Monitoring and Contaminants Research Vol.1, pp.17–27 (2021) DOI: https://doi.org/10.5985/emcr.20200004

Environmental Monitoring & Contaminants Research Environmental Monitoring and Contaminants Research Vol.1, pp.17–27, 2021 https://emcr-journal.org/ Article Contamination levels, monthly variations, and predictions of neonicotinoid pesticides in surface waters of Gifu Prefecture in Japan

Yoshitaka HAYASHI1), Nozomi SASAKI2), Mari TAKAZAWA3)＊, Tomomi INAGAKI4), Hiroki NAKAMURA4), Atsushi YAMAMOTO1) and Shigeru SUZUKI1)

1) Graduate School of Bio Sciences and Bio Technologies, Chubu University, 1200 Matsumoto, Kasugai City, Aichi Prefecture 487-8501 Japan 2) School of Public Health, Department of Environmental Health Science, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222 USA 3) Water Environment Research Group (Water Quality), Public Works Research Institute, 1-6 Minamihara, Tsukuba City, Ibaraki Prefecture 305-8516 Japan 4) Gifu Research Center for Public Health, 4-6 Akebono, Gifu City, Gifu Prefecture 500-8148 Japan

[Received September 28, 2020; Accepted November 5, 2020]

ABSTRACT The neonicotinoid pesticides acetamiprid (ACE), clothianidin (CTD), dinotefuran (DIN), imidacloprid (IMI), nitenpyram (NTP), thiacloprid (THI), and thiamethoxam (TMX) are widely used in over 120 countries. These pesticides have been regulated in many jurisdictions, including the European Union (EU), the United States, and the United Kingdom, due to adverse effects on non-target organisms, whereas some of these pesticides are permitted in Japan. In the present study, we have 1) measured levels of these pesticides at 103 locations (n = 672) across Gifu Prefecture, 2) analyzed the monthly trends and regionality using R and ArcGIS, and 3) created a predicted contamination map by an ordinary kriging analysis. The concentration levels of the seven neonicotinoid pesticides in surface waters were determined using liquid chromatography with tandem mass spectrometry (LC/ MS/MS) and ranged from < 2.0 to 530 ng/L during the ten-month period. In a total of 672 samples, the top three pesticides detected at high frequency were DIN (76.9%), CTD (48.4%), and IMI (19.6%). The concentration of the neonicotinoid pesticides in environmental waters varied with the time periods of application, physiochemical properties of the pesticides, land use, geological properties of the contamination sources, and other factors. Potential contamination sources were depicted in the predicted contamination maps by using ordinary kriging models, which showed that DIN and CTD are widely present in Gifu Prefecture. Monthly variance of the concentration of IMI differed in the two geological regions, due to differenc- es in the time of application and agricultural products yield. The results of our study contribute to a better understanding of the contamination status of neonicotinoid pesticides by providing reference data (actual pesticide concentrations) as well as predicted contamination maps.

Key words: neonicotinoid pesticide; surface water; Gifu Prefecture; predicted contamination; environmental behavior; dinotefuran

ﬂowers and fruits (National Institute for Environmental INTRODUCTION Studies, 2018). The seven major neonicotinoid pesticides are Neonicotinoid pesticides are currently the most widely acetamiprid (ACE; CAS RN®: 160430-64-8), clothianidin (CTD; used class of pesticides in over 120 countries (Simon-Delso et CAS RN®: 210880-92-5), dinotefuran (DIN; CAS RN®: 165252- al., 2015). In Japan, an estimated 431 tons of neonicotinoid pes- 70-0), imidacloprid (IMI; CAS RN®: 138261-41-3), nitenpyram ticides were used in 2018 in the production of rice, vegetables, (NTP; CAS RN®: 150824-47-8), thiacloprid (THI; CAS RN®: 111988-49-9) , and thiamethoxam (TMX; CAS RN®: 153719-23- ＊ Corresponding Author: [email protected] 4) , all of which are hydrophilic in nature (Chemicalize, 2020). This article is licensed under a Creative Commons [Attribution These pesticides dissolve in water and enter vegetation 4.0 International] license. © 2021 The Authors. through the roots, and, in small quantities, these pesticides are

Japan Society for Environmental Chemistry Neonicotinoid concentrations in surface waters effective insecticides (Wood and Goulson, 2017). Nicotinoids Chemical Co., Inc. (Tokyo, Japan). Pure water ( ≤ 0.5 μS/m) are considered to be less toxic to vertebrates than other com- was purchased from ADVANTEC (Tokyo, Japan). Solid phase monly used pesticides and are relatively safer for the environ- extraction cartridges (InertSep Slim-J Pharma FF, 230 mg), ment (Nauen et al., 2001; Tomizawa and Casida, 2005) due to and graphite carbon-SPE cartridges (InertSep Slim-GC, 400 their specific mode of action as inhibitors of insect nicotinic mg) were purchased from GL Sciences (Tokyo, Japan) and acetylcholine receptors (nAChRs) (Palmer et al., 2013). How- both were conditioned with 5 mL acetone and 10 mL pure ever, the findings of prior studies suggest that these pesticides water prior to use. L-column 2 (2.1 mm × 150 mm, 3 μm) was may have adverse direct or indirect effects on children and purchased from Chemicals Evaluation and Research Institute non-target organisms in the environment (Hallmann et al., (Tokyo, Japan). 2014; Blacquière et al., 2012; Millot et al., 2017; Van Dijk et al., 2013; Han et al., 2018; Woodcock et al., 2016; Gupta, 2018). SAMPLE COLLECTION AND EXTRACTION Due to the adverse effects of neonicotinoid pesticides, the Surface water samples were collected at 37 locations of governments of several European countries and the United seven major river systems (Ibi-, Jintsuu-, Kiso-, Nagara-, Shou-, States (US) have established restrictions. The governments of Shonai-, and Yahagi-river) and 66 locations in their tributaries the US and the United Kingdom restricted the use of all neon- (n = 672) in Gifu Prefecture, Japan, from May 2016 to February icotinoid pesticides in 2017 (US Congress, 2017; Department 2017 (Fig. S1). The samples were collected after the approval for Environment, Food and Rural Affairs, 2017). In 2018, the of local laws, and the GPS coordinates of the sampling loca- European Union (EU) banned all outdoor uses of the three ne- tions are given in Table 1. All water samples were collected at onicotinoid pesticides (CTD, IMI, and THI) (European Com- the surfaces of the center streams and stored in polypropylene mission, 2018). In contrast, Japanese regulators, with the aim bottles in a refrigerator at 4°C until sample extraction. of promoting widespread application of neonicotinoids in agri- The extraction procedure of the neonicotinoid pesticides cultural production, had loosened relevant laws in 2015 (Minis- in surface water is shown in Fig. S2. The pesticides in the sam- try of Health, Labour and Welfare, 2015) and in 2017 (Ministry pled water were collected by passing 250 mL of the water of Health, Labour and Welfare, 2017). Therefore, particularly through an InertSep Slim-J Pharma FF cartridge (Pharma in Japan, evaluating the effects of neonicotinoid pesticides on FF). After loading the samples, an InertSep Slim-GC (Slim-GC) human bodies, organisms, and the environment is important. cartridge was connected to the exit of the sample containing There have been a number of reports in Japan of contami- Pharma FF, the pesticides were eluted by passing 5 mL ace- nation from these pesticides in surface waters (Yamamoto et tone through the Pharma FF connected to the Slim-GC. The al., 2012; Sato et al., 2016; Nishino et al., 2018), effluents eluted solution was collected in a glass tube and concentrated (Nishino et al., 2018), tap water (Sato et al., 2016; Kamata et al., to 0.1 mL or less under a gentle stream of nitrogen gas. The 2020), and underground water sources (Hayashi et al., 2017). concentrated solution was reconstituted with ACN/water Due to the hydrophilic nature of nicotinoids, there is the possi- (1/9, v/v) to 1 mL, and 1 μL of this solution was applied to bility that these pesticides, when applied to agricultural fields, LC/MS/MS measurement. will enter the environment through runoff (Pietrzak et al., 2020). Most of the agricultural fields in Japan are used to grow INSTRUMENTAL CONDITIONS rice, and 70% of these are “wet fields.” (Ministry of Agriculture, The pesticides were analyzed by selected reaction moni- Forestry and Fisheries, 2019). Thus, runoff of hydrophilic pes- toring (SRM) with an API5500 electrospray tandem quadru- ticides from rice paddy fields is a major concern when examin- pole mass spectrometer (Sciex Pte., Ltd., Framingham, MA, ing contamination of the aquatic environment. USA) interfaced with LC800 HPLC systems (GL Sciences Inc., In this study we carried out a monthly survey (over 10 Tokyo, Japan) equipped with L-column 2 and mobile phase of months) of the occurrence of seven neonicotinoid pesticides ACN and 0.1 v/v% formic acid in water. The details of the in- (ACE, CTD, DIN, IMI, NTP, THI, and TMX) in surface waters strumental parameters are presented in Table S1. Analyte at 103 locations in seven river systems across Gifu Prefecture. peaks were identified with the retention times ( ± 0.05 min), We found that nicotinoids had been widely applied to rice pad- and the ratio of quantitative to quantitative transition-ion re- dy fields, vegetable fields, orchards, and golf courses. We used sponses ( ± 20%) as well as predefined sets of SRM transitions. geostatistical analysis (ArcGIS) with ordinary kriging to obtain ACE, TMX, and IMI were quantified with deuterium-labeled predicted contamination distribution maps of CTD, DIN, and standards as the surrogate, and the other pesticides were

IMI across Gifu Prefecture. quantified using acetamiprid-d3 as the clean-up spike. Five- point calibration curves ranging from 0.5 to 10 μg/L were MATERIALS AND METHODS made. The regression coefficients (r2) with equal weighting MATERIALS AND CHEMICALS quadratic regression were ≥ 0.999. Analytical standards of each neonicotinoid pesticide mixture (20 mg/L each in acetonitrile [ACN]), as well as deute- VALIDATION OF THE QUANTITATIVE ANALYSIS rium-labeled standards of thiamethoxam-d4, imidacloprid-d4, The analytical method was validated throughout the entire and acetamiprid-d3, and formic acid of LC/MS grade (99.5%) analysis. A volume of surface water (250 mL each, n = 3) was were purchased from Wako Pure Chemica Industries (Osaka, spiked with 10 ng of each of the seven pesticides, and 1 ng of Japan). Standard stock solutions were prepared in ACN at each mixture of the three deuterium-labeled standards was 1,000 μg/L and stored in a freezer at − 20°C. Acetone and used for evaluating the recovery efficiencies. Non-spiked sur- ACN for pesticide residual grade were purchased from Kanto face waters (procedural blanks) were prepared and analyzed.

18 Environmental Monitoring and Contaminants Research Vol.1, pp.17–27 (2021)

The neonicotinoid pesticides were not found in the surface wa- ng/L on average), followed by CTD (12.0 ng/L on average), ter, thus the surface water was sufficient for quality matrix TMX (8.6 ng/L on average), and IMI (7.5 ng/L on average). spike experiments. ACE, NIT, and THI were rarely detected above the LOD thresholds (5.8, 3.5, 5.0 ng/L on average, respectively). GEOSTATISTICAL ANALYSIS OF POTENTIAL SOURCES Throughout this 10-month period, we detected DIN and CTD OF PESTICIDE CONTAMINATIONS at the highest frequencies (76.9% and 48.4%, respectively). Gifu Prefecture, located in central Japan, can be divided Monthly concentration profiles of the seven neonicotinoid into five basic geological regions: Chuunou, Gifu, Hida, Seinou, pesticides in the seven major river systems in Gifu Prefecture and Tounou (Gifu Prefecture, 2019). There are seven major are shown in Fig. 1. Each of the Y-axis values was normalized river systems: Ibi, Jintsuu, Kiso, Nagara, Shonai, Shou, and by the ratio of the neonicotinoid concentration divided by an- Yahagi. Potential neonicotinoid contamination sources are: nual average concentration of each neonicotinoid from all the rice fields, vegetable crops, fruit and mulberry trees, and tea samples. Surface water samples of the Shou-river were collect- cultivation. These areas were identified using the GIS land use ed from locations deep in the mountainous area, away from shape files (specific to Gifu Prefecture) provided by the human activities. Consequently, the neonicotinoid pesticides Ministry of Land, Infrastructure, Transport, and Tourism were detected in only three samples from the Shou-river (Ministry of Land, Infrastructure, Transport and Tourism, throughout the ten-month period (including trace levels of 2018; Ministry of Land, Infrastructure, Transport and Tourism, DIN and CTD). 2018). The other land use type in our analysis is golf courses, In all river systems, the concentration ratios of DIN were which were identified from GPS information of Google Maps close to 1.0, meaning there were higher concentrations in Au- (Google LLC, Califronia, USA). gust followed by October and November, except for samples Areas with potential contamination by neonicotinoid pesti- from the Shou-river system (Fig. 1). Similarly, the concentra- cides were calculated using ArcGIS (Environmental Systems tion of CTD tended to be higher in samples collected in May in Research Institute, ver. 10.5) and then overlayed on maps all the river systems except the Shou-river system. In the showing the agricultural areas and golf courses. At each sam- Yahagi-river system, the concentration ratios of CTD were pling point, annual average concentrations of CTD, DIN, and above 1.0 during most of the study period. Both the Kiso- and IMI were calculated using R (R Core Team, 3.6) and plotted Yahagi-river systems exhibited concentration ratios of TMX at with locations in ArcMap by coordinating with GCS_JGD_2011. higher concentrations from October to December. In the Kiso- Concentrations of CTD, DIN, and IMI in non-sampling areas and Nagara-river systems, ratio trends of IMI stayed above 1.0 were predicted by geostatistical analysis with ordinary kriging for more than four months and concentrations in this period method. The weighted average of adjacent sampling locations, were higher than the ten-month average. These detection pat- and the distribution of predicted values of pesticide concentra- terns reveal monthly trends in neonicotinoid concentrations in tions were plotted in the maps of Gifu Prefecture. We used a surface water. cross-validation procedure to ensure high accuracy and low bias in our maps (Boken et al., 2004; Santra et al., 2008; CORRELATION BETWEEN CONCENTRATIONS OF Buchanan and Triantafilis, 2009). This analysis focused on pre- NEONICOTINOID PESTICIDES AND LAND USE IN GIFU dicting average annual concentrations of the neonicotinoid PREFECTURE pesticides in Gifu Prefecture without considering other envi- LAND USE AND AREAS OF POTENTIAL CONTAMINATION ronmental factors such as flow speed, precipitation, and SOURCES OF THE NEONICOTINOID PESTICIDES monthly trends. In Gifu Prefecture (9,562 km2), the area of potential contamination sources is 1,057 km2, including rice paddies (817 RESULTS AND DISCUSSION km2), vegetable farms (133 km2), golf courses (42.9 km2), fruit VALIDATION OF THE QUANTITATIVE ANALYSIS orchards (33.3 km2), mulberry tree cultivation (25.3 km2), and The average recoveries and their relative standard devia- tea fields (6.1 km2). The corresponding areas were calculated tions are 92.8%–99.3% and 0.9%–1.8%, respectively. In Table S2 for each of the five regions within Gifu Prefecture: Chuunou, we present the values for recovery rate and the limit of detec- Gifu, Hida, Seinou, and Tounou (Table 2). tion (LODs), which were calculated by dividing the instrumen- Hida is one of the typical mountainous regions in Japan tal detection limit (IDLs) with the sample volume. Each of the and although it is the largest geological area in Gifu Prefecture IDL values were determined as the concentration at which the (as shown in Table 2), the farmable area is very small (144 chromatographic peak signal was five times higher than the km2). Compared to Hida, the other regions, namely Seinou, baseline noise. For quality assurance, a concentration of the Gifu, Chuunou, and Tounou, have larger farming areas and standard mixture was injected after every 15 of the sample golf courses, and, therefore, more frequent use of neonicoti- measurements. In the quality assurances, the target recovery noid pesticides is expected in these regions. In Fig. 2 we show value was 100% ± 20%. the distribution of the potential sources of contamination of the neonicotinoid pesticides. The distribution pattern of contami- LC/MS/MS DETECTION OF NEONICOTINOID nation sources in rice paddy fields and vegetables farms is PESTICIDES IN SURFACE WATERS very similar, and both were spread over the entire area of Gifu Annual average concentrations of the neonicotinoid pesti- Prefecture (with the exception of Hida region, as explained cides in surface water at sampling sites (n = 672) are shown in above). Golf courses were mainly located in Chuunou and Tou- Table 1. DIN was detected at the highest concentrations (22.8 nou, fruits orchards and tea fields in Seinou, and mulberry

19 Neonicotinoid concentrations in surface waters

Table 1 Annual average concentrations of seven neonicotinoid pesticides in surface water of the seven major river systems in Gifu Prefecture

d) d) d) d) d) d) d) River Sample GPS ACE CTD DIN IMI NTP TMX THI M/Ta) Regionb) Sampling location nc) system ID East North (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L)

1 Ibi IB-1 T S Haginaga bridge 136.57069 35.47377 10 <2 <2 <2 <2 <2 <2 <2 2 Ibi IB-2 M G Godo bridge 136.64259 35.39404 3 <2 <2 <2 <2 <2 <2 <2 3 Ibi IB-3 T S Neo bridge 136.63326 35.43091 3 <2 <2 11.3 <2 <2 <2 <2 4 Ibi IB-4 T S Mimizugawa bridge 136.63141 35.43113 10 <2 4.7 45.6 3.2 <2 <2 <2 5 Ibi IB-5 T S Hanada river 136.63081 35.43249 3 <2 4 30.3 2.3 <2 <2 <2 6 Ibi IB-6 T S Shimozakura bridge 136.63664 35.41516 3 <2 <2 15 <2 <2 <2 <2 7 Ibi IB-7 M S Ohgaki bridge 136.65319 35.34413 3 <2 <2 3.7 <2 <2 <2 <2 8 Ibi IB-8 T S Hachibe bridge 136.62032 35.32984 4 <2 30.8 3 <2 <2 <2 <2 9 Ibi IB-9 T S Ai river 136.58166 35.33745 10 <2 5.6 22.4 2.2 <2 <2 <2 10 Ibi IB-10 T S Ichinose bridge 136.48036 35.31659 10 <2 6.9 10.7 <2 <2 <2 <2 11 Ibi IB-11 T S Nakazu river 136.64156 35.31351 3 <2 4.3 14.7 <2 <2 <2 <2 12 Ibi IB-12 M S Fukuoka bridge 136.61682 35.22171 3 <2 <2 6.7 <2 <2 <2 <2 13 Ibi IB-13 T S Fukuoka ohashi bridge 136.60945 35.22564 10 <2 5.5 91.5 2.7 <2 <2 <2 14 Ibi IB-14 M S Kaidu bridge 136.62974 35.18653 3 <2 <2 8.3 <2 <2 <2 <2 15 Ibi IB-15 T S Manju bridge 136.65679 35.16888 4 2.8 3.8 239 5.3 <2 <2 <2 16 Ibi IB-16 M O Ise bridge 136.69006 35.08046 3 <2 2 14 <2 <2 <2 <2 17 Ibi IB-17 M O Most downstream of Ibi-river 136.70315 35.06101 2 <2 <2 10 <2 <2 <2 <2

18 Jintsuu JI-1 M H Ichinomiya bridge 137.24809 36.08668 10 <2 2.8 7.7 <2 <2 <2 <2 19 Jintsuu JI-2 T H Daihachi river 137.25868 36.15482 3 <2 11.7 33 3.3 <2 2.3 <2 20 Jintsuu JI-3 T H Kawakami river 137.24111 36.16997 6 <2 8 71.8 2 <2 <2 <2 21 Jintsuu JI-4 T H Kohachiga river 137.26234 36.17891 10 <2 2.8 13.8 2.5 <2 <2 <2 22 Jintsuu JI-5 T H Arashiro river 137.18493 36.23337 9 <2 13.7 33.6 <2 <2 <2 <2 23 Jintsuu JI-6 M H Miyashiro bridge 137.18184 36.23632 10 <2 7.6 35.2 3.5 <2 <2 <2 24 Jintsuu JI-7 T H Odori dam 137.01456 36.22456 4 <2 <2 <2 <2 <2 <2 <2 25 Jintsuu JI-8 T H Odori river 137.10790 36.30510 6 <2 2.5 4.3 <2 <2 <2 <2 26 Jintsuu JI-9 M H Shinkokkyo bridge 137.24724 36.46166 10 <2 4.7 22.5 <2 <2 <2 <2 27 Jintsuu JI-10 T H Asaida Tsutsumiseki 137.35222 36.29784 10 <2 <2 <2 <2 <2 <2 <2 28 Jintsuu JI-11 T H Shin-Inotani river 137.27973 36.42880 10 <2 <2 <2 <2 <2 <2 <2

29 Kiso KI-1 T T Kawakami river 137.52618 35.57170 10 <2 <2 7.1 <2 <2 <2 <2 30 Kiso KI-2 T T Ochiai river 137.52843 35.51844 10 <2 <2 5.7 <2 <2 <2 <2 31 Kiso KI-3 M T Ochiai dam 137.52989 35.52722 10 <2 <2 <2 <2 <2 <2 <2 32 Kiso KI-4 T T Nakagawa bridge 137.50209 35.48210 10 <2 <2 <2 <2 <2 <2 <2 33 Kiso KI-5 T T Nakatsu river 137.49695 35.50845 10 <2 <2 2.8 <2 <2 <2 <2 34 Kiso KI-6 M T Mie bridge 137.45813 35.50645 4 <2 <2 <2 <2 <2 <2 <2 35 Kiso KI-7 T T Shiranui river 137.44604 35.53535 10 <2 <2 14.2 <2 <2 <2 <2 36 Kiso KI-8 T T Agigawa dam 137.43355 35.41877 4 <2 5.3 10.5 <2 <2 5.8 <2 37 Kiso KI-9 T T Ena ohhashi bridge 137.41933 35.44792 9 <2 4.2 11.2 <2 <2 4.3 <2 38 Kiso KI-10 T T Agi river 137.40251 35.46609 10 <2 5 10.7 4 <2 3.9 <2 39 Kiso KI-11 T T Tomoe bridge 137.33210 35.48088 10 <2 4.4 28.4 <2 <2 <2 <2 40 Kiso KI-12 M C Kaneyama dam 137.10658 35.46645 10 <2 <2 4.5 <2 <2 <2 <2 41 Kiso KI-13 T H Takanedaiichi dam 137.49305 36.03183 3 <2 <2 <2 <2 <2 <2 <2 42 Kiso KI-14 T H Takanedaini dam 137.46299 36.02935 3 <2 <2 <2 <2 <2 <2 <2 43 Kiso KI-15 T H Asahi dam 137.41254 36.07649 3 <2 <2 <2 <2 <2 <2 <2 44 Kiso KI-16 T H Furuko bridge 137.27349 35.94099 10 <2 <2 <2 <2 <2 <2 <2 45 Kiso KI-17 T H Higashiueda 137.23359 35.82814 10 <2 <2 4.5 <2 <2 <2 <2 46 Kiso KI-18 T T Iwaya dam 137.43355 35.41877 4 <2 <2 <2 <2 <2 <2 <2 47 Kiso KI-19 T H Maze river 137.15903 35.66320 10 <2 <2 11.6 <2 <2 <2 <2 48 Kiso KI-20 T C Iwaana bridge 137.19949 35.58334 10 <2 2.4 10.7 <2 <2 <2 <2 49 Kiso KI-21 T C Shirakawa river 137.19300 35.58356 10 <2 <2 11.3 <2 <2 <2 <2 50 Kiso KI-22 T C Hisen 137.18790 35.57830 4 <2 2.8 10.3 <2 <2 <2 <2 51 Kiso KI-23 T C Kawabe dam 137.07304 35.48462 10 <2 <2 6.3 <2 <2 <2 <2 52 Kiso KI-24 M C Shin-ohta bridge 137.03274 35.43671 3 <2 <2 4.7 <2 <2 <2 <2 53 Kiso KI-25 T C Kamo river 137.00199 35.43136 10 <2 18.2 92.8 4.5 <2 7.6 <2 54 Kiso KI-26 T C Kigan bridge 137.19186 35.41087 4 <2 <2 <2 <2 <2 <2 <2 55 Kiso KI-27 T C Toyaba bridge 137.05457 35.41365 10 <2 62.2 48.9 7.3 <2 15.5 <2 56 Kiso KI-28 T C Hane bridge 137.00874 35.41985 10 <2 51.4 43.7 7 <2 15.7 <2 57 Kiso KI-29 M G Inuyama bridge 136.94512 35.39323 3 <2 <2 5 <2 <2 <2 <2 58 Kiso KI-30 T O Shingose river 136.95324 35.38015 2 <2 8 15 <2 <2 3 <2 59 Kiso KI-31 T O Gose river 136.95287 35.38177 1 <2 3 5 23 <2 <2 <2 60 Kiso KI-32 T G Higashiizumi bridge 136.86877 35.42020 10 <2 4.5 73.1 10 <2 4 <2 61 Kiso KI-33 M G Kiso ohhashi bridge 136.76094 35.36259 3 <2 2 6.7 <2 <2 <2 <2 62 Kiso KI-34 M S Tokai bridge 136.68082 35.22616 3 <2 2 7.3 <2 <2 <2 <2 63 Kiso KI-35 M O Most downstream of Kiso-river 136.71323 35.10509 2 <2 <2 4.5 <2 <2 <2 <2

64 Nagara NA-1 M C Mukaiyama bridge 136.84499 35.91430 4 <2 4.8 9.3 <2 <2 <2 <2 65 Nagara NA-2 M C Wago bridge 136.91095 35.78636 10 <2 <2 33.6 <2 <2 <2 <2 66 Nagara NA-3 T C Ono bridge 136.97357 35.75508 10 <2 <2 7.2 <2 <2 <2 <2 67 Nagara NA-4 T C Ohnara bridge 136.95000 35.71303 3 <2 <2 2.3 <2 <2 <2 <2 68 Nagara NA-5 T C Nagase bridge 136.90620 35.56775 10 <2 <2 <2 <2 <2 <2 <2 69 Nagara NA-6 M C Shimowatari bridge 136.89847 35.54355 4 <2 <2 9 <2 <2 <2 <2 70 Nagara NA-7 M C Ayunose bridge 136.89114 35.50345 10 <2 <2 15.4 <2 <2 <2 <2 71 Nagara NA-8 T C Minamimuge bridge 136.85367 35.51679 10 <2 <2 5.8 <2 <2 <2 <2

20 Environmental Monitoring and Contaminants Research Vol.1, pp.17–27 (2021)

d) d) d) d) d) d) d) River Sample GPS ACE CTD DIN IMI NTP TMX THI M/Ta) Regionb) Sampling location nc) system ID East North (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L) (ng/L)

72 Nagara NA-9 T C Kawaura river 136.97590 35.48664 4 <2 32.8 37 11.5 <2 <2 <2 73 Nagara NA-10 T C Sakura bridge 136.85707 35.47144 10 <2 14 34.3 3.3 <2 <2 <2 74 Nagara NA-11 M G Kagashima bridge 136.72812 35.41897 3 <2 <2 12 <2 <2 <2 <2 75 Nagara NA-12 T G Take bridge 136.71228 35.42397 3 <2 <2 33 4 <2 <2 <2 76 Nagara NA-13 T G Naeda bridge 136.69054 35.40115 10 <2 <2 11 10.4 <2 <2 <2 77 Nagara NA-14 T S Sai river 136.68588 35.33650 3 <2 <2 9.7 2.7 <2 <2 <2 78 Nagara NA-15 T G Gyaku river 136.68978 35.33187 4 <2 <2 14.5 11.5 <2 <2 <2 79 Nagara NA-16 M G Nanno bridge 136.67463 35.27096 3 <2 <2 11.3 <2 <2 <2 <2 80 Nagara NA-17 T G Kuwabara river 136.68008 35.26517 10 <2 <2 14.1 8 <2 <2 <2 81 Nagara NA-18 M S Tokai bridge 136.67357 35.22513 3 <2 <2 13 <2 <2 <2 <2 82 Nagara NA-19 M O Ise bridge 136.69506 35.08329 3 <2 <2 11.7 <2 <2 <2 <2 83 Nagara NA-20 M O Most downstream of Nagra-river 136.71403 35.05672 2 <2 <2 9 2 <2 <2 <2

84 Shou SH-1 M H Makido 136.94829 36.04746 4 <2 <2 <2 <2 <2 <2 <2 85 Shou SH-2 M H Miboro dam 136.90931 36.13612 3 <2 <2 <2 <2 <2 <2 <2 86 Shou SH-3 M H Naride dam 136.87497 36.35054 6 <2 <2 <2 <2 <2 <2 <2

87 Shonai SN-1 M T Mizunami bridge 137.25984 35.36757 10 <2 10.8 20.6 <2 <2 2.7 <2 88 Shonai SN-2 T T Kaore bridge 137.29298 35.32582 4 <2 30.5 10 <2 <2 4 <2 89 Shonai SN-3 T T Harako bridge 137.26060 35.36697 10 <2 17.4 15.3 <2 <2 3.3 <2 90 Shonai SN-4 T T Hida bridge 137.20625 35.36190 10 <2 <2 5.8 <2 <2 <2 <2 91 Shonai SN-5 M T Sankyo bridge 137.20438 35.36302 10 <2 13.1 22.5 <2 <2 <2 <2 92 Shonai SN-6 T T Miyuki bridge 137.17392 35.35053 10 <2 2.2 2.4 <2 <2 <2 <2 93 Shonai SN-7 T T Sakura bridge 137.12333 35.32870 10 <2 3.1 2.3 <2 <2 <2 <2 94 Shonai SN-8 M T Kuninaga bridge 137.11726 35.32952 3 <2 9.7 23.3 <2 <2 2 <2 95 Shonai SN-9 T T Ohhara river 137.11528 35.33047 3 <2 4.7 6 <2 <2 2 <2 96 Shonai SN-10 M T Toki river 137.10241 35.31567 3 <2 8.3 23.7 <2 <2 2.7 <2

97 Yahagi YA-1 T T Sekirei bridge 137.48961 35.26051 10 <2 <2 <2 <2 <2 <2 <2 98 Yahagi YA-2 M T Ohkawa bridge 137.47172 35.24819 10 <2 <2 5.6 <2 <2 <2 <2 99 Yahagi YA-3 M O Sasado dam 137.36722 35.23389 3 <2 6 10.3 <2 <2 3.3 <2 100 Yahagi YA-4 T T Akechi river 137.38323 35.24675 10 <2 12.1 14 <2 <2 7 <2 101 Yahagi YA-5 T O Azuma river 137.37220 35.23920 10 <2 23 3.4 <2 <2 22.3 <2 102 Yahagi YA-6 M O Ayumi bridge 137.32156 35.21600 2 <2 7.5 16.5 <2 <2 3 <2 103 Yahagi YA-7 M O Shidare bridge 137.21081 35.14376 2 <2 6.5 16.5 <2 <2 2.5 <2 a) M: Main stream T: Tributary b) C: Chuunou G: Gifu H: Hida S: Seinou T: Tounou O: Outside of Gifu-prefecture c) n: Sample size d) ACE: acetamiprid CTD: clothianidin DIN: dinotefuran IMI: imidacloprid NTP: nitenpyram THI: thiacloprid TMX: thiamethoxam

tree plantations in Chuunou and Seinou. systems in August. Since the annual concentrations of DIN (Fig. 3) approximately correspond to the areas of rice fields REGIONAL AND MONTHLY VARIATION IN (Table 2), we infer that a large amount of DIN application to NEONICOTINOID CONTAMINATION AND MAPPING OF rice fields in August would increase DIN concentrations in the PREDICTED CONTAMINATION surface water. CTD, DIN, IMI, and TMX were the most frequently detect- In the DIN predicted-distribution map (RMSE = 0.98, ed neonicotinoid pesticides (as shown in Table 1). The annual ME = − 0.07), the locations of rice paddy field and vegetable averages, medians, minimums, and maximums of the four pes- farms are concordant with higher concentrations of DIN ( > 30 ticides in the five geological regions are shown in Fig. 3. Using ng/L) in the Northern Hida, Southern Seinou, Southern Gifu, the quantitative values, geostatistical analysis (ArcGIS) with and Southern Chuunou (Fig. 4). In most parts of Gifu Prefecture, ordinary kriging was carried out to create maps of predicted the concentrations were predicted to be more than 2.98 ng/L. contamination distribution of the whole Gifu Prefecture for This result is possibly due to the continuous activities of rice CTD, DIN, and IMI. We note that the prediction of TMX con- and vegetable farms and suggests that these two land use centration distributions could not be achieved using ordinary types are probable sources of DIN. The distribution of predict- kriging analysis due to the low detection rate. ed DIN concentration raises concerns about the adverse health effects from chronic DIN exposure. The no-ob- DINOTEFURAN (DIN) served-adverse-effect-levels for pregnant women has been de- The range of annual average concentrations and the detec- termined to be 784 mg/kg/d by the US Environmental tion rates of DIN were < 2.0–239 ng/L and 50.0%–93.8% in Protection Agency (Sheets et al., 2015), and because some each sampling location. DIN and CTD have been used for ex- residents of Gifu Prefecture are still using well-water for drink- terminating insect pests of rice (Lanka et al., 2014). Generally, ing water, monitoring of concentrations of DIN runoff in sur- in Japan, DIN is applied to rice fields around August (Tsueda face and underground water sources would help to prevent et al., 2002; Suzuki, 2005; Hashimoto, 2005), and in the present adverse health effects. study, DIN was found at high concentrations in all the river

21 Neonicotinoid concentrations in surface waters

Shou Jintsuu

1.0 1.0

0.1 0.1 ug ug May May A Nov Nov Feb May May A Feb

Nagara Kiso

1.0 1.0

0.1 0.1 ug May May Nov A Feb Nov May May Aug Feb

Ibi Shonai

1.0 1.0

0.1 0.1 ug May May Aug Nov Nov Feb May May A Feb

Yahagi Acetamiprid Clothianidin Dinotefuran 1.0 Imidacloprid Nitenpyram Thiacloprid 0.1 Thiamethoxam Nov May May Aug Feb X axis： month Y axis：concentration ratio (detected concentration/annual average concentration) Fig. 1 Monthly concentration proﬁles of the seven neonicotinoid pesticides in the seven major river systems in Gifu Prefecture

Table 2 Areas of potential neonicotinoid contamination sources in the five geological regions of Gifu Prefecture Gifu-prefecture Chuunou Gifu Hida Seinou Tounou (km2) (km2) (km2) (km2) (km2) (km2) Whole area 9,562 2,193 798 4,033 1168 1,371 of Gifu prefecture Farming and golf courses Rice fields 817 185 147 112 234 140 Vegetable farms 133 39.6 29.6 25.7 13.1 24.5 Golf courses 42.9 23.5 3.8 0.9 0 14.6 Fruits farms 33.3 2.9 12 2.9 10.3 5.2 Mulberry tree fields 25.3 7.7 2.8 2.7 5.2 7 Tea fields 6.1 1.7 0.1 0.4 3.5 0.4 Total 1,057 261 195 144 266 192

22 Environmental Monitoring and Contaminants Research Vol.1, pp.17–27 (2021)

2 2 2 Rice fields: 817 km Vegetable farms: 133 km Golf courses: 42.9 km N

W E

2 2 2 Fruits farms: 33.3 km Mulberry tree fields: 25.3 km Tea fields: 6.1 km

0 20 40 60 80 (km) Fig. 2 Area distributions of potential sources of neonicotinoid pesticide contamination in Gifu Prefecture

clothianidin dinotefuran imidacloprid thiamethoxam (ng/L) 1000

100

1 ABCDE ABCDE ABCDE ABCDE

A…Chuunou B…Gifu C…Hida D…Seinou E…Tounou Fig. 3 Ten-month average concentrations of four neonicotinoid pesticides in surface waters of Gifu Prefecture. The ﬁve geological regions are indicated by letters A–E

dinotefuran clothianidin imidacloprid Filled Filled Filled Contours Contours Contours (ng/L) (ng/L) (ng/L)

0 0 0

10 30 5

100 60 10

0 10 20 40 60 (km) Fig. 4 Contamination prediction maps of three of the neonicotinoid pesticides in Gifu Prefecture

CLOTHIANIDIN (CTD) mum values of 62.2 and 30.5 ng/L, respectively) than in the The range of annual average concentration and the detec- other regions. The CTD concentrations in a tributary of the tion rates of CTD were < 2.0–62.2 ng/L and 20.0%–60.0% in Yahagi-river system, which is located close to a golf course, each sampling location. each sampling location. Although showed a different monthly change from the other sampling CTD is applied to rice ﬁelds, the regional annual average con- points. Golf courses are an expected source of contamination centration proﬁle was different from that of DIN (Fig. 3). CTD due to the fact that most golf courses in Gifu Prefecture are concentrations were higher in Chuunou and Tounou (maxi- located in Chuunou and Tounou, and these golf courses are

23 Neonicotinoid concentrations in surface waters often near tributaries and rivers. For this reason we would ex- vegetable farms. pect higher concentrations of CTD in these water bodies. The map of predicted IMI distribution (RMSE = 0.97, From the predicted CTD distribution map (RMSE = 0.99, ME = − 0.04) was produced by a cross-validation procedure. ME = 0.30), areas of higher concentrations ( > 30 ng/L) in IMI has been observed in two hot spots in the Southern Gifu, Southern Chuunou and Tounou were also matched with the where elevated concentrations could be due to the high yield locations of golf courses (Fig. 4). However, in the Northern of agricultural products in Chuunou and Gifu. Hida region, this pattern of high concentration of CTD did not correspond to the proximity of golf courses. It is likely that el- THIAMETHOXAM (TMX) evated concentration of CTD in surface waters in Northern The range of the annual average concentrations and the Hida are due to other contamination sources, in particular rice detection rates of TMX were < 2.0–15.7 ng/L and 0%–40%, re- and vegetable cultivation areas, as indicated by their distribu- spectively, in each sampling location. TMX was found only in tion. CTD is found in most areas in Gifu Prefecture, and golf Tounou. Although TMX has been reported to be a precursor of courses are the likely sources (Bradford et al., 2018). The ad- CTD (Nauen et al., 2003), the regional annual average concen- verse health effects from chronic exposure to low-dose CTD tration profile was not similar to that of CTD. These results are still unknown. As an intervention in cases of residential suggest that TMX might be quickly metabolized in environ- chronic exposure to CTD, the monitoring of CTD concentra- ment or might be applied for another crops. tions in surface and underground water sources should be considered. ESTIMATING THE ENVIRONMENTAL BEHAVIOR OF THE NEONICOTINOID PESTICIDES IMIDACLOPRID (IMI) The neonicotinoid pesticides, as well as other organic The range of annual average concentrations and the detec- chemicals, tend to adsorb on black carbon in soil (Motoki et tion rate of IMI were < 2.0–11.5 ng/L and 4.0%–50.0%, respec- al., 2015). However, factors affecting the adsorption of the pes- tively, in each sampling location. IMI was found at higher fre- ticide molecules to black carbon in soil are complex and un- TM quencies in Gifu and Chuunou, where vegetable farms are clear. We used Log Koc (with EPI Suite , USEPA), with the larger in size than in other regions (Table 2). IMI was found at organic carbon/water partition coefficient, to briefly examine concentrations higher than 10 ng/L only in Chuunou and Gifu the adsorption/desorption behavior of the neonicotinoid pesti- (sites #72, NA-9 and #78, NA-15 in Table 1), downstream of the cides in an aqueous environment. In Table 3 we list the concen-

Table 3 Annual detection levels of the neonicotinoid pesticides in underground water and three comparable

factors (chemical structure, Log Koc, and Log Kow) possibly affecting levels

Name NTP THI ACE CTD IMI TMX DIN underground water < 0.3 < 0.3 < 0.3 0.3 0.8 0.9 8.6 average＊＊ (ng/L, n = 118)

Log Koc 3.2 3.1 2.7 3.0 3.0 2.4 2.1

Log Kow 0.4 2.3 2.6 0.6 0.6 0.8 − 0.2 Half-life of photolysis in NA 10–63 34 <1 <1 2.7–40 <2 water

(DT50 in days) Half-life of hydrolysis in Stable 2.9 Stable 420 Stable 14 Stable > 1yr Stable 12 Stable Stable water (pH9) (pH9) (pH9) (pH9) (pH9)

(DT50 in days)

＊ A, B, C: Substructures of increasing Koc value (A: + 0.39 B: + 0.22 C: + 0.18) ＊ D: Substructure of decreasing Koc value (D: − 0.09) ＊＊ average concentrations of summer and winter surveys (2 days) ＊＊＊ Morrissey et al. (2015) NTP: nitenpyram THI: thiacloprid ACE: acetamiprid IMI: imidacloprid CTD: clothianidin TMX: thiamethoxam DIN: dinotefuran

24 Environmental Monitoring and Contaminants Research Vol.1, pp.17–27 (2021) tration levels of the neonicotinoid pesticides in underground water through summer/winter surveys (Hayashi et al., 2017) DECLARATION OF COMPETING INTEREST with three comparable factors (chemical structure, Log Koc, The authors declare that they have no known competing and Log Kow) possibly affecting the levels. Pesticides contain- financial interests or personal relationships that could have ap- ing a nitrile-group showed the strongest adsorption onto soil peared to influence the work reported in this paper. particles (Substructure A in Table 3), followed by nitro-group (Substructure B), and pyridine-ring (Substructure C). NTP, SUPPLEMENTARY MATERIAL THI, and ACE, which were not found in underground water Fig. S1, Sampling locations and the river systems in Gifu through summer/winter surveys (Hayashi et al., 2017), have Prefecture; Fig. S2, Extraction procedure for seven neonicoti- multiple substructures of A–C within their chemical struc- noid pesticides in surface water samples; Table S1, Instrumen- tures. The above results suggest that pesticide molecules sub- tal parameters of the HPLC/MS/MS measurement; Table S2, stituted with nitrile-group, nitro-group, and pyridine-ring were Recoveries and limits of detection of the seven neonicotinoid rarely detected in underground waters due to their high affini- pesticides in surface water (n = 3). ty to soil components. It has been suggested that molecules This material is available on the Website at https://doi. with a nitrile-group in their structures might be adsorbed on org/10.5985/emcr.20200004. black carbons in soil by π–π interaction (Bucheli and Gustafsson, 2003; Sobek et al., 2009). On the other hand, the annual aver- REFERENCES age concentrations of CTD, IMI, TMX, and DIN in under- Blacquière, T., Smagghe, G., Van Gestel, C.A.M., Mommaerts, ground water sources were found to be in the range of 0.3–8.6 V., 2012. Neonicotinoids in bees: a review on concentrations, ng/L (Hayashi et al., 2017). This might be due to substructures, side-effects and risk assessment. Ecotoxicology 21, 973–992. making it harder to be adsorbed by soil particles, or hydrophilic doi: 10.1007/s10646-012-0863-x. substructures decreasing the Log Koc value (Substructure D in Boken, V.K., Hoogenboom, G., Hook, J.E., Thomas, D.L.,

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