Occurrence of neonicotinoid pesticides in outdoor air particulate matters from 2019 to 2021 in China

doi:10.21203/rs.3.rs-4106234/v1

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Occurrence of neonicotinoid pesticides in outdoor air particulate matters from 2019 to 2021 in China

https://doi.org/10.21203/rs.3.rs-4106234/v1

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Neonicotinoid insecticides (NNIs), as a new type of insecticide, are now widely used in agriculture and daily life. Because of the problematic volatility of NNIs, few studies have evaluated them in atmospheric particulate matter. In this study, a total of 101 outdoor PM2.5 samples were collected from the Wuhan urban area from 2019 to 2021, and seven NNIs and three metabolites of NNIs were detected. The frequency of detection for all 10 substances was more than 60%, with DIN and IMI reaching 100%. DIN (53.3 pg/m³) and IMI (42.8 pg/m³) had higher median concentrations than did the other substances. The distributions of four neonicotinoids and one metabolite were significantly different (p<0.05) across the four seasons: spring, summer, fall and winter. The median concentration of imidacloprid-equivalent total neonicotinoids (IMIeq: generated by the relative potency factor method) was 256.1 pg/m³. Finally, the estimated daily intake (EDI) of NNIs via respiration was greater in infants and young children than in the rest of the population, suggesting that infants and young children are more likely to be exposed to the health effects of airborne residual PM2.5.

Neonicotinoids (NNIs) are novel pesticides that function primarily through the selective control of insect nervous system nicotinic acetylcholine esterase receptors, thereby disrupting the normal conduction of the insect central nervous system and resulting in paralysis and mortality of the pest(Matsuda et al. 2001). NNIs include acetamiprid (ACE), clothianidin (CLO), dinotefuran (DNT), imidacloprid (IMI), thiacloprid (THCP), thiamethoxam (THM) and nitenpyram (NTP)(Chi-Hsuan Chang et al. 2018). Compared with conventional insecticides, NNIs possess notable advantages, such as safety, efficacy, and high selectivity, allowing them to be extensively utilized in agriculture and even in our daily lives(D. Chen et al. 2020). There were 12 kinds of NNIs registered in China as of September 2021; more than 120 countries were reported to have NNIs registered, and NNIs accounted for 25% of the global insecticide market in 2014(Bass et al. 2015).

However, excessive use of NNIs has been reported to have adverse effects on ecosystems and human health(Christen, Mittner, and Fent 2016). IMI can cause colony collapse syndrome, which reduces the ability of bees to forage(Cox-Foster et al. 2007). In addition, NNIs are lethal to aquatic and terrestrial invertebrates and potentially dangerous to mammals(Li, Miao, and Khanna 2020), including humans(Vijver and van den Brink 2014). In addition, environmental media such as food, water, and air have been extensively contaminated by the overuse of NNIs, suggesting that humans could be exposed to NNIs through a variety of routes, such as ingestion, drinking, and breathing(Han, Tian, and Shen 2018).

China is now the largest producer and pesticide consumer in the world(Xiao 2003). As the largest sale and application amount among NNIs in China, IMI's annual production reached 13,500 tons in 2010(Shao et al. 2013). However, the mass production and use of NNIs have also brought certain disadvantages. In China, pesticides are usually sprayed, which inevitably leads to missed targets and released material into the air. According to statistics, the amount of pesticide lost into the air during spraying can reach 30–50%(Coscollà et al. 2008). Ambient air particulate matter, especially small particles, are likely to adsorb toxic and hazardous substances(Valavanidis, Fiotakis, and Vlachogianni 2008). However, it has been shown that NNI residues in atmospheric dust account for 0.01–0.40% of the actual amount applied(Montiel-León et al. 2019). Although NNIs are found at low levels in the atmosphere, hazards are not underestimated. Atmospheric NNIs not only kill insects such as bees and butterflies but also cause secondary pollution to water bodies through transportation and deposition. As a result, exposure to fine particles can cause a range of harmful respiratory(Liu et al. 2017), cardiovascular(S.-Y. Chen, Chan, and Su 2017), neurological(M. Chen, Li, and Sang 2017), and inflammatory effects(Staffolani et al. 2015) in humans.

In this study, we collected atmospheric PM2.5 samples from the Wuhan urban area from 2019–2021. The year-season concentration levels and distribution characteristics of 10 NNIs affecting PM2.5 were determined and analysed, and the respiratory exposure levels and potential health risks of the NNIs affecting PM2.5 in the study area were preliminarily evaluated in combination with population exposure parameters, which provided data support air quality evaluation and control of new organic pollutants.

2.1. Standards and reagents

Standards of NNIs, including ACE, CLO, DNT, IMI, NIT, THM, FLO, N-ACE, 5-OH-IMI, and 6-ClNA, were acquired from Aladdin, America. The corresponding isotope labelling products were purchased from Anpel Laboratory Technologies (Shanghai) Inc. The above standard solutions were stored in cold storage away from light. Acetonitrile, methanol, n-hexane, and dichloromethane (LC‒MS grade) were obtained from Fisher Chemical (Thermo Fisher Scientific, Inc., Waltham, MA, USA). A 2 mL automatic sampling bottle, cap and PTFE silicone gasket were purchased from Agilent Technology.

2.2. Sample collection

Outdoor PM2.5 samples were collected using a TH-150C medium-volume air sampler, with a continuous sampling duration of 24 hours on a 90 mm diameter quartz filter membrane. The sampling flow rate was set at 100 L/min. Sampling was conducted from January 2019 to September 2021, resulting in a total of 101 collected samples. The sampling site was located on the rooftop of Building 2, School of Public Health, Tongji Medical College (30°35′N, 114°15′E), approximately 12 metres above ground level, with no industrial emission sources in the vicinity. Before sampling, the sampling filters were precleaned, and the instrument’s gas flow rate was calibrated using a flow metre. Meteorological data such as atmospheric pressure, temperature, and humidity were recorded during the sampling process. After sampling, the filter membranes containing the collected air particulate matter were folded, placed in self-sealing bags and stored at -20°C in a freezer for subsequent analysis.

2.3. Sample preparation

A quarter section of the PM2.5-loaded filter membrane was cut and transferred to a glass centrifuge tube. Then, 20 µL of a mixed internal standard solution was added. Then, 3 mL of dichloromethane/hexane (3/1, v/v) was added, followed by ultrasonic extraction at room temperature for 30 min at 3000 rpm. The mixture was then centrifuged for 5 min. After centrifugation, the supernatant was transferred to a new glass test tube. Subsequently, another 3 mL of dichloromethane/hexane was added, followed by a repeat of ultrasonic extraction. The second extraction was combined with the first extraction. Next, 3 mL of acetonitrile was added to the filter membrane sample, and the same ultrasonic extraction process was repeated twice. Finally, the total extracted solution from the four transfers was dried using a 40°C water bath nitrogen evaporator, after which 200 µL of methanol was added for reconstitution. After vortexing at 2500 rpm for 1 min using a plate shaker, 100 µL of the reconstituted solution was filtered through a 0.22 µm organic membrane and transferred to a sealed refrigerated sample vial containing 200 µL of a microinsert. The resulting sample was subjected to LC‒MS/MS analysis.

2.4. Instrumental analysis

Ultra-performance liquid chromatography-tandem mass spectrometry (UPLC‒MS/MS, Waters ACQUITY UPLC I-Class coupled with Xevo TQS, Waters Corporation, Milford, MA, USA) was performed in electrospray ionization positive mode and multiple reaction monitoring (MRM) mode. The MRM transitions and collision energies of the target NNIs are presented in Table S1. Chromatographic separation was achieved on an ACQUITY UPLC BEH C18 column (2.1 × 100 mm, 1.7 µm). The column temperature was maintained at 40°C. The mobile phase consisted of water containing 0.1% formic acid (mobile phase A) and methanol (mobile phase B). Linear gradient elution was carried out at a flow rate of 0.3 mL/min. The total program time was 10 min, and the injection volume was 5 µL. The analyte liquid chromatography elution procedures are shown in Table S2.

2.5. Quality assurance and quality control

In this study, analytes were quantified using an internal standard calibration method. Twelve calibration levels (0.1 ng/mL-1000 ng/mL) were used for instrument calibration. The linearity (r²) of all the calibration curves was > 0.990. The limits of detection for the ten analytes ranged from 0.01 to 0.07 ng/mL. The recoveries and relative standard deviations of the spiked samples were calculated to assess the accuracy and precision, respectively. The results showed that the recoveries of the spiked analytes were in the range of 85.96%-107.53%, and the relative standard deviations of the parallel measurements were less than 15%, which met the requirements of quantitative analysis.

2.6. Exposure assessment in the populations

2.6.1. Calculation of the relative potency factor (RPF)

As shown in the following equation, the relative potency factor (RPF) method was used to normalize each NNI to the IMI to integrate the cumulative exposure and risk of individual NNIs via the same mode of action by inhibiting nAChRs in outdoor PM2.5 samples. The IMI was chosen as a reference NNI due to its extensive use and research(C.-H. Chang et al. 2018). The following formulae were obtained as follows:

IMIeq: imidacloprid-equivalent total neonicotinoids, IMIeq = IMI (NNI_i × RPF_i) = clothianidin × 5.82 + imidacloprid × 1 + dinotefuran × 2.85 + acetamiprid × 0.80 + thiamethoxam × 9.50.

The calculation of RPF was based on the chronic reference dose (cRfD) of imidacloprid(C.-H. Chang et al. 2018; Lu et al. 2018) as shown in Table S3.

2.6.2. The calculation of estimated daily intake (EDI)

The primary route of entry for hazardous substances in atmospheric particulate matter into the human body is through inhalation via the respiratory system. The estimated daily intake (EDI) is computed based on the following formulaic model(U.S. Environmental Protection Agency.1992. 2014):

$${\text{EDI(}\text{inh}\text{.)}}_{a}\text{= }\frac{{\text{C}}_{\text{a}}\text{×IR×RR×}{\text{ABS}}_{\text{a}}\text{×ET×EF×ED}}{\text{BW×AT}}\text{#1-1}$$

The equation above represents the estimation model for calculating the estimated daily intake (EDI) of each target substance (denoted as EDI(inh.)_a) via inhalation, expressed in picograms per kilogram of body weight per day (pg/kg·day). In the equation, C_a represents the concentration of the target substance a in the air (pg/m3), IR and BW denote the inhalation rate (m³/day) and body weight (kg), respectively, RR refers to the respiratory retention rate (%), ABS_a represents the percentage of the target substance a absorbed into the bloodstream (%), ET represents the duration of exposure (h/day), EF refers to the exposure frequency (days/years), and ED denotes the duration of exposure (years). AT represents the average exposure time (in days). For noncarcinogenic effects, AT corresponds to the number of days of exposure in EDI. The values for other exposure parameters can be found in Table S4.

2.7. Statistical analyses

The data analysis was completed with IBM® SPSS Statistics 26. NNI concentrations less than the detection limit were replaced by a root 2 of the detection limit. Differences between groups were analysed by one-way ANOVA when the two sets of data were normally distributed; otherwise, the Mann‒Whitney U test was used. Pearson correlation tests were performed to explore the relationships between each neonicotinoid pesticide. A level of statistical significance was defined as α = 0.05.

3.1. Overall detection of NNIs in PM2.5 samples from Wuhan.

Seven NNIs, namely, CLO, ACE, NIT, DIN, FLO, IMI, and THM, as well as three metabolites, 5-OH-IMI, N-ACE, and 6-CINA, were analysed in 101 PM2.5 samples. Table 1 summarizes the detection frequency (DF), detection range, geometric mean (GM), median and mean of these analytes. Among the PM2.5 samples collected, the detection frequencies of all 10 substances reached more than 60%. IMI and DIN reached 100%, followed by 5-OH-IMI (98%), N-ACE (98%), and ACE (97%), and then FLO (94.1%), NIT (88.1%), THM (77.2%), CLO (75.2%), and 6-CINA had the lowest detection rate (64.45%). In terms of the median concentration, DIN (53.3 pg/m³) and IMI (42.8 pg/m³) had higher concentrations than did the other substances. Moreover, the average concentrations of these two are also relatively high, reaching 194.1 pg/m³ and 71.6 pg/m³, respectively.

Table 1

Concentration (pg/m³) and detection frequencies of NNIs in PM.5 samples from 2019 to 2021 in Wuhan.
NNIs	DF	GM	Median	Average	Range
CLO	75.2%	4.4	8.3	8.0	<LOD-24.3
ACE	97.0%	2.8	3.3	14.9	<LOD-1106.1
NIT	88.1%	1.7	2.2	63.9	<LOD-5671.7
DIN	100.0%	59.0	53.3	194.1	7.8-4389.4
FLO	94.1%	2.9	2.8	3.5	<LOD-10.4
IMI	100.0%	43.0	42.8	71.6	5.0-368.9
THM	77.2%	1.3	1.1	2.6	<LOD-20.6
5-OH-IMI	98.0%	11.4	10.6	16.1	<LOD-103.9
N-ACE	98.0%	5.3	5.6	6.2	<LOD-22.8
6-CINA	64.45%	0.4	0.6	10.3	<LOD-850.0
IMIeq	/	251.3	256.1	708.0	/
DF: detection requency; GM: geometric mean; Range: means minimum to max-imum concentration

Overall, there was a correlation between the frequency and concentration of substances detected in PM2.5 samples and the amount of production and use in the actual situation. For example, the high detection frequency of IMI (100%) also confirms that IMI are the most frequently used NNIs(ltd n.d.). In China, the total ACE productivity is 8000 tons per year(Shao et al. 2013). Notably, ACE was also detected at a higher frequency and concentration in this study. This finding is also consistent with the results of a national study. Wang et al.(Wang et al. 2019) collected dust from three cities, Taiyuan, Wuhan and Shenzhen. It was found that NNIs and their metabolites were widely present in dust from the three cities, with ACE and IMI having the highest detection rates. The metabolite of ACE, N-ACE, was detected about as often (98%) as ACE (97%) itself, and even the median and geometric concentrations (GM) of N-ACE were higher than those of ACE itself. In addition, 5-OH-IMI also exhibited a high detection frequency and concentration. This suggests that NNIs are readily metabolized to specific metabolites through biotransformation (demethylation, hydroxylation, and nitro reduction)(Casida 2011; Taira, Fujioka, and Aoyama 2013). Thus, NNIs and their metabolites cooccur in the environment. Different metabolic pathways result in different metabolites, leading to significant differences in toxicity among NNI metabolites(Marfo et al. 2015). The acute and chronic thresholds of 5-OH-IMI have been reported to be significantly greater than those of IMI itself(Suchail, Debrauwer, and Belzunces 2004). Thus, the high detection frequency of 5-OH-IMI in the environment should be considered. In addition, imine derivatives of IMI were more than 300 times more toxic to vertebrates based on the half maximal inhibitory concentration (IC₅₀)(Tomizawa and Casida 2000). In-depth studies on NNI metabolites are also necessary.

3.2. The results of PM2.5 samples stratified by season

NNIs were stratified by season and analysed for variability in concentrations between seasonal stratification groups. The results of the analysis are shown in Table 2. The 101 PM2.5 samples were categorized into spring (n = 27), summer (n = 15), autumn (n = 33), and winter (n = 26) according to the time of collection. The distributions of ACE, DIN, IMI, THM, and 5-OH-IMI significantly differed among the four seasons (P < 0.05). The median concentrations of DIN remained high during the spring (53.33 pg/m³), autumn (58.33 pg/m³), and winter (76.11 pg/m³). In addition, IMI presented the highest median concentration in the summer (129.44 pg/m³), followed by spring (82.78 pg/m³). Detailed information is provided in Table S5. Moreover, its metabolite 5-OH-IMI exhibited the same trend. The seasons with a high frequency of pesticide use are usually spring and summer. This is because in spring and summer, warmer temperatures and increased humidity favour the reproduction and spread of pests. Crops also experience peak growth during these times, increasing susceptibility to pest infestation. Therefore, most of the NNIs in our study maintained high concentrations during the spring and summer months. However, our findings suggested that there are also a number of NNIs that had higher levels in the winter months, similar to NIT (3.6 pg/m³) and DIN (76.11 pg/m³). We speculate that the possible reason for this difference is that the near-surface atmosphere is relatively stable in the fall and winter months, particulate matter floating in the air tends to accumulate, and it also facilitates the formation of aerosols of organic compounds and other pollutants or their full attachment to suspended particulate matter in the atmosphere(Achar et al. 2020). Therefore, higher concentrations were maintained in the collected PM2.5 samples. In addition, the variability in the distribution of concentrations of NNIs and their metabolites in different seasons may also be controlled by chemical processes and regional sources, which will be mentioned in the following paragraphs.

Table 2

Analysis of concentration disparities of various NNIs across the four seasons (pg/m³)
	Spring(n = 27)	Summer(n = 15)	Autumn(n = 33)	Winter(n = 26)	P
CLO	8.33	7.78	7.78	8.89	0.169
CLO	(7.22, 15.00)	(6.67,9.44)	(< LOD,9.44)	(< LOD,12.92)	0.169
ACE	3.33	3.89	1.67	4.17	< 0.001
ACE	(2.22,4.10)	(2.78,4.44)	(1.39,2.98)	(3.24,6.8)	< 0.001
NIT	2.22	1.11	1.90	3.06	0.917
NIT	(1.11,4.98)	(1.11,4.19)	(0.56,5.00)	(0.42,7.08)	0.917
DIN	53.33	22.22	58.33	76.11	< 0.05
DIN	(20.56,100.00)	(20.00,28.3)	(27.78,121.67)	(43.19,135.54)	< 0.05
FLO	3.33	2.78	2.78	3.33	0.844
FLO	(2.22,5.00)	(2.78,3.33)	(2.78,3.33)	(2.78,4.44)	0.844
IMI	82.78	129.44	16.11	51.67	< 0.001
IMI	(32.78,171.11)	(34.44,182.22)	(10.28,35.56)	(26.25,127.5)	< 0.001
THM	0.56	0.56	0.56	2.78	0.034
THM	(< LOD,2.22)	(< LOD,2.22)	(< LOD,2.50)	(0.56,7.78)	0.034
5-OH-IMI	13.89	9.44	11.11	8.33	0.029
5-OH-IMI	(8.89,30.77)	(8.33,12.22)	(6.67,18.61)	(6.67,19.58)	0.029
N-ACE	5.98	5.00	5.00	5.74	0.062
N-ACE	(4.44,8.89)	(3.33,5.56)	(3.61,6.39)	(3.75,9.17)	0.062
6-ClNA	0.56	1.11	0.56	1.39	0.214
6-ClNA	(< LOD,1.11)	(0.56,1.67)	(< LOD,1.11)	(< LOD,3.61)	0.214

3.3. Correlation analysis between the NNIs detected in the PM2.5 samples

Spearman correlation analysis of 10 analytes detected in PM2.5 samples revealed positive correlations between CLO and IMI, 5-OH-IMI, and N-ACE (p < 0.05) but a negative correlation between CLO and THM (p < 0.05). ACE was positively associated with NIT, DIN, IMI, THM, N-ACE, and 6-CINA (p < 0.05). A positive correlation was observed between the NIT and DIN concentrations (p < 0.01). DIN was positively associated with 5-OH-IMI and N-ACE (p < 0.05). In addition, FLO was positively correlated with IMI, 5-OH-IMI and N-ACE (p < 0.05). IMI was positively correlated with N-ACE (p < 0.001), and 5-OH-IMI was positively correlated with N-ACE (p < 0.001) (Fig. 1). Most of the 10 analytes were positively correlated, suggesting that they might share a common source of environmental contamination, and the negative correlations between several targets suggest that they may be substitutes for each other in practical applications. The average concentration of CLO (8.0 pg/m³) was greater than that of THM (2.6 pg/m³), and there was a negative correlation between CLO and THM, with a correlation coefficient of 0.2 (p < 0.05); this may be because in addition to CLO itself, THM in the environment can be transformed into CLO(N et al. 2015).

3.4. The distribution characteristics of the compositions of the 10 NNIs in the PM2.5 samples in the different seasons.

The distribution characteristics of the compositions of the 10 NNIs in PM2.5 during the different seasons are shown in Fig. 2. The distributions of NNI components in the four seasons were similar. These products were mainly composed of IMI, DIN, 5-OH-IMI and CLO. Among them, IMI was the most dominant NNI in summer and spring, and the proportions reached 71% and 47%, respectively. DIN was the most dominant NNIs in autumn and winter, with 55% and 46%, respectively. 5-OH-IMI and CLO had comparable compositional distributions in the four seasons.

Residues of NNIs and their metabolites in the environment are affected by multiple factors, such as physical and chemical properties, external environmental conditions and regions. In terms of their physicochemical properties, the water solubility of different kinds of NNIs varies. NNIs have small molecular weights and high solubility and are generally between 185 and 590000 mg/L(Dores et al. 2008). In addition, NNIs have strong water solubility. All these factors make NNIs highly mobile in various environmental media. Moreover, because NNIs are polar insecticides, their volatility is low. Therefore, NNIs are generally found at low concentrations in the atmosphere but are more abundant in soil and water environments. The water solubility of NNIs is also related to temperature. For example, the water solubility of THM was 340 mg/L, while that of FLO reached as high as 39830 mg/L at 20°C(Yi et al. 2019). We speculate that when the water solubility of NNI decreases, it may more frequently reach environmental media other than water, such as the atmosphere. Thus, the concentrations and species distributions of NNIs in atmospheric particulate matter vary under different seasonal conditions and with changes in temperature. In terms of regional distribution, a study of drinking water in eight cities across the country showed that CLO, IMI, and THM are the predominant NNIs in South China, Central China, and Southwest China, while ACE, IMI, and THM are the major NNIs in East China and Northeast China. ACE and IMI are the most significant NNIs in Northwest China and North China(Mahai et al. 2021). Different provinces and regions grow different types of crops, which may result in different types and doses of NNIs being used. During the widespread application of NNIs, only approximately 5% of the active ingredient is absorbed by crops, 90% of the active ingredient enters the soil, and the remainder is dispersed into water and the atmosphere(Giorio et al. 2021; Zhang et al. 2018). Some NNIs in soil are enriched in soil partly by adsorption, partly by desorption into farm water, and migrate to rivers, lakes and wetlands through irrigation leakage. In contrast, NNIs in the atmosphere are produced mainly by particulate matter formed during the sowing of coated seeds and during pesticide spraying. Pesticide spraying, in turn, is related to the type of crop grown. According to the website of the China Pesticide Information Network, NNIs registered in China are mainly used for rice and wheat(2022), with relatively few used for other crops, such as corn, fruit trees, and vegetables. As a result, in provinces or regions where rice and wheat are predominantly grown, environmental media tend to have higher levels of NNIs.

3.5. Inhalation exposure in different populations

Moreover, NNI in PM2.5 might be exposed to populations via inhalation and increase health risks. Based on the target concentrations obtained at the monitoring sites, the median and geometric mean of the concentrations were used to calculate the EDI for the low-exposure scenario, and the 95th percentile was used to calculate the EDI for the high-exposure scenario. The EDIs for different populations exposed to NNIs are shown in Table S6. Figure 3 shows that ACE, CLO, IMI and DIN are the main targets for population exposure. The daily exposure dose per unit body weight to NNIs in the population was much greater in the high-exposure scenario than in the low-exposure scenario. The daily exposure dose per unit body weight for children in both exposure scenarios was 1.3 times that of adult residents and 2.8 times that of adult workers. IMI and DIN had high EDIs in both exposure scenarios.

Humans are exposed to NNIs mainly through diet, drinking water, soil/dust and respiratory intake. Diet and drinking water intake may be the main routes of human exposure to NNIs. However, respiratory intake is also a route that should not be underestimated. NNIs can be sprayed directly on crop surfaces or taken up by plants after application and distributed in tissues and organs, where pollen can be dispersed into the ambient air by windborne vectors or pollinators. A test of NNIs in pollen samples collected from 35 hives in North America revealed THM in 19 samples, ACE in 11 samples, and IMI in 10 samples(Mullin et al. 2010). NNIs are commonly found in pollen, and airborne pollen can be ingested by humans through respiration, and once inhaled, it may be adsorbed in the human respiratory tract and lungs due to its high water solubility. In Hernandez et al.’s study, respiratory functions were measured and compared between 89 pesticide sprayers and 25 non-spraying control farmers in southeastern Spain. The results suggested a relationship between neonicotinoid application and lung dysfunction (lower total lung capacity, residual volume and functional residual capacity)(Hernández et al. 2008). Therefore, research on the risks to human health from respiratory exposure should be strengthened. Most of the previous studies have focused on the effects of the NNI on the respiratory health of occupational populations during agricultural activities(Hernández et al. 2008). However, in our study, infants and young children, a sensitive population, were more susceptible to health effects due to NNI exposure.

3.6. Limitations

At present, most domestic and international studies on NNIs have focused on other environmental media, such as water, soil, sediment and living organisms, and there are few reports on NNIs in atmospheric particulate matter. This study has several limitations. The study selected samples from only the outdoor atmosphere of the Wuhan urban area, and the sample size was limited, which cannot reflect the atmospheric NNI pollution in the whole city. In addition, there are many types of NNIs, and only 10 common NNIs and their metabolites were selected for the study. The concentrations of other NNIs are not yet known. More importantly, the study focused only on the outdoors, while NNIs were also detected at high concentrations in indoor dust samples(Wang et al. 2019). Finally, a comprehensive assessment of the human health effects of neonicotinoid pesticides requires the simultaneous evaluation of multiple environmental media, especially in drinking water.

In this study, we measured the concentrations of NNIs in 101 outdoor PM2.5 sources in urban areas of Wuhan from 2019 to 2021, analysed their distributions in different seasons, and evaluated the daily exposures of the population. Although the volatility of the NNI is low, its concentration in the atmosphere is relatively high. With the widespread use of NNIs, population inhalation health risks may increase. In addition, the risk of exposure to NNIs is particularly high for sensitive populations such as infants and young children. In conclusion, sustained monitoring of the risk of NNIs for air is essential.

Author Contribution

Du was responsible for the main manuscript text and the completion of the experiments, Zhu was involved in the production of the figures and tables, Yao was responsible for the planning of the experiments and the preliminary research, and all authors reviewed the manuscript

Acknowledgements

This work was supported by the Contract grant sponsor: Science and Technology Program of Shenzhen, China (Contract grant number: JCYJ20180301170716407)

Achar JC, Nam G, Jung J, Klammler H, Mohamed MM (2020) Microbubble Ozonation of the Antioxidant Butylated Hydroxytoluene: Degradation Kinetics and Toxicity Reduction. Environ Res 186:109496
Bass C, Denholm I, Williamson MS, Nauen R (2015) The Global Status of Insect Resistance to Neonicotinoid Insecticides. Pestic Biochem Physiol 121:78–87
Casida JE (2011) Neonicotinoid Metabolism: Compounds, Substituents, Pathways, Enzymes, Organisms, and Relevance. J Agric Food Chem 59(7):2923–2931
Chang C-H, Macintosh D, Lemos B, Zhang Q, Lu C (2018) Characterization of Daily Dietary Intake and the Health Risk of Neonicotinoid Insecticides for the U.S. Population. J Agric Food Chem 66(38):10097–10105
Chang Chi-Hsuan D, MacIntosh B, Lemos Q, Zhang, Lu C (2018) Characterization of Daily Dietary Intake and the Health Risk of Neonicotinoid Insecticides for the U.S. Population. J Agric Food Chem 66(38):10097–10105
Chen D, Zhang Y, Lv B, Liu Z, Han J, Li J, Zhao Y, Wu Y (2020) Dietary Exposure to Neonicotinoid Insecticides and Health Risks in the Chinese General Population through Two Consecutive Total Diet Studies. Environment International 135.
Chen M, Li B, Sang N (2017) Particulate Matter (PM2.5) Exposure Season-Dependently Induces Neuronal Apoptosis and Synaptic Injuries. J Environ Sci 54:336–345
Chen S-Y, Chan C-C, Su T-C (2017) Particulate and Gaseous Pollutants on Inflammation, Thrombosis, and Autonomic Imbalance in Subjects at Risk for Cardiovascular Disease. Environ Pollut 223:403–408
Christen V, Mittner F, Fent K (2016) Molecular Effects of Neonicotinoids in Honey Bees (Apis Mellifera). Environ Sci Technol 50(7):4071–4081
Coscollà C, Yusà V, P Martí, and, Pastor A (2008) Analysis of Currently Used Pesticides in Fine Airborne Particulate Matter (PM 2.5) by Pressurized Liquid Extraction and Liquid Chromatography–Tandem Mass Spectrometry. J Chromatogr A 1200(2):100–107
Cox-Foster DL, Conlan S, Holmes EC, Palacios G, Evans JD, Moran NA, Quan P-L, Briese T, Hornig M, Geiser DM, Martinson V, vanEngelsdorp D, Kalkstein AL, Drysdale A, Hui J, Zhai J, Cui L, Hutchison SK et al (2007) A Metagenomic Survey of Microbes in Honey Bee Colony Collapse Disorder, vol 318. Science, pp 283–287. (New York, N.Y.)5848
Dores EFGC, Carbo L, Ribeiro ML, De-Lamonica-Freire EM (2008) Pesticide Levels in Ground and Surface Waters of Primavera Do Leste Region, Mato Grosso, Brazil. J Chromatogr Sci 46(7):585–590
Giorio C, Safer A, Sánchez-Bayo F, Tapparo A, Lentola A, Girolami V, van Lexmond MB, Bonmatin J-M (2021) An Update of the Worldwide Integrated Assessment (WIA) on Systemic Insecticides. Part 1: New Molecules, Metabolism, Fate, and Transport. Environ Sci Pollut Res Int 28(10):11716–11748
Han W, Tian Y, Shen X (2018) Human Exposure to Neonicotinoid Insecticides and the Evaluation of Their Potential Toxicity: An Overview. Chemosphere 192:59–65
Hernández AF, Casado I, Pena G, Gil F, Villanueva E, Pla A (2008) Low Level of Exposure to Pesticides Leads to Lung Dysfunction in Occupationally Exposed Subjects. Inhalation Toxicol 20(9):839–849
Li Y, Miao R, Khanna M (2020) Neonicotinoids and Decline in Bird Biodiversity in the United States. Nat Sustain 3(12):1027–1035
Liu S, Zhou Y, Liu S, Chen X, Zou W, Zhao D, Li X, Pu J, Huang L, Chen J, Li B, Liu S, Ran P (2017) Association between Exposure to Ambient Particulate Matter and Chronic Obstructive Pulmonary Disease: Results from a Cross-Sectional Study in China. Thorax 72(9):788–795
ltd R (2023) December 13, and M. Production and Market of Imidacloprid in China - Research and Markets. https://www.researchandmarkets.com/reports/4755745/production-and-market-of-imidacloprid-in-china
Lu C, Chang C-H, Palmer C, Zhao M, Zhang Q (2018) Neonicotinoid Residues in Fruits and Vegetables: An Integrated Dietary Exposure Assessment Approach. Environ Sci Technol 52(5):3175–3184
Mahai G, Wan Y, Xia W, Wang A, Shi L, Qian X, He Z, Xu S (2021) A Nationwide Study of Occurrence and Exposure Assessment of Neonicotinoid Insecticides and Their Metabolites in Drinking Water of China. Water Res 189:116630
Marfo JT, Fujioka K, Ikenaka Y, Nakayama SMM, Mizukawa H, Aoyama Y, Ishizuka M, Taira K (2015) Relationship between Urinary N-Desmethyl-Acetamiprid and Typical Symptoms Including Neurological Findings: A Prevalence Case-Control Study. PLoS ONE 10(11):e0142172
Matsuda K, Buckingham SD, Kleier D, Rauh JJ, Grauso M, Sattelle DB (2001) Neonicotinoids: Insecticides Acting on Insect Nicotinic Acetylcholine Receptors. Trends Pharmacol Sci 22(11):573–580
Montiel-León JM, Munoz G, Vo Duy S, Do DT, Vaudreuil M-A, Goeury K, Guillemette F, Amyot M, Sauvé S (2019) Widespread Occurrence and Spatial Distribution of Glyphosate, Atrazine, and Neonicotinoids Pesticides in the St. Lawrence and Tributary Rivers. Environmental Pollution (Barking, Essex: 1987) 250: 29–39
Mullin CA, Frazier M, Frazier JL, Ashcraft S, Simonds R, D vanEngelsdorp, and, Pettis JS (2010) High Levels of Miticides and Agrochemicals in North American Apiaries: Implications for Honey Bee Health ed. Frederic Marion-Poll. PLoS ONE 5(3):e9754
N S-D, A-R V B, Lp B, Jm CM, Dw DCFLG, Dp GCGVGDK, Ch K, Ea LMLEMMMPM et al (2015) Systemic Insecticides (Neonicotinoids and Fipronil): Trends, Uses, Mode of Action and Metabolites. Environmental science and pollution research international 22(1). https://pubmed.ncbi.nlm.nih.gov/25233913/ (January 2, 2024)
National Bureau of Statistics of China (2022) China Statistical Yearbook 2022. https://www.stats.gov.cn/sj/ndsj/2022/indexeh.htm (January 9, 2024)
Shao X, Liu Z, Xu X, Li Z, Qian X (2013) Overall Status of Neonicotinoid Insecticides in China: Production, Application and Innovation. J Pesticide Sci 38(1):1–9
Staffolani S, Manzella N, Strafella E, Nocchi L, Bracci M, Ciarapica V, Amati M, Rubini C, Re M, Pugnaloni A, Pasquini E, Tarchini P, Valentino M, Tomasetti M, Santarelli L (2015) Wood Dust Exposure Induces Cell Transformation through EGFR-Mediated OGG1 Inhibition. Mutagenesis 30(4):487–497
Suchail S, Debrauwer L, Belzunces LP (2004) Metabolism of Imidacloprid in Apis Mellifera. Pest Manag Sci 60(3):291–296
Taira K, Fujioka K, Aoyama Y (2013) Qualitative Profiling and Quantification of Neonicotinoid Metabolites in Human Urine by Liquid Chromatography Coupled with Mass Spectrometry. PLoS ONE 8(11)
Tomizawa M, Casida JE (2000) Imidacloprid, Thiacloprid, and Their Imine Derivatives Up-Regulate the Α4β2 Nicotinic Acetylcholine Receptor in M10 Cells. Toxicol Appl Pharmcol 169(1):114–120
U.S. Environmental Protection Agency.1992. O. (2014) Guidelines for Exposure Assessment. Guidelines for exposure assessment. https://www.epa.gov/risk/guidelines-exposure-assessment (2024)
Valavanidis A, Fiotakis K, Vlachogianni T (2008) Airborne Particulate Matter and Human Health: Toxicological Assessment and Importance of Size and Composition of Particles for Oxidative Damage and Carcinogenic Mechanisms. J Environ Sci Health C 26(4):339–362
Vijver MG, van den Brink PJ (2014) Macro-Invertebrate Decline in Surface Water Polluted with Imidacloprid: A Rebuttal and Some New Analyses. PLoS ONE 9(2):e89837
Wang A, Mahai G, Wan Y, Jiang Y, Meng Q, Xia W, He Z, Xu S (2019) Neonicotinoids and Carbendazim in Indoor Dust from Three Cities in China: Spatial and Temporal Variations. Sci Total Environ 695:133790
Xiao X (2003) Uncertainties in Estimates of Cropland Area in China: A Comparison between an AVHRR-Derived Dataset and a Landsat TM-Derived Dataset. Glob Planet Change 37:297–306
Yi X, Zhang C, Liu H, Wu R, Tian D, Ruan J, Zhang T, Huang M, Ying G (2019) Occurrence and Distribution of Neonicotinoid Insecticides in Surface Water and Sediment of the Guangzhou Section of the Pearl River, South China. Environmental Pollution (Barking, Essex: 1987) 251: 892–900
Zhang P, Ren C, Sun H, Min L (2018) Sorption, Desorption and Degradation of Neonicotinoids in Four Agricultural Soils and Their Effects on Soil Microorganisms. Sci Total Environ 615:59–69

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Occurrence of neonicotinoid pesticides in outdoor air particulate matters from 2019 to 2021 in China

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Abstract

Figures

1. Introduction

2. Materials and methods

2.1. Standards and reagents

2.2. Sample collection

2.3. Sample preparation

2.4. Instrumental analysis

2.5. Quality assurance and quality control

2.6. Exposure assessment in the populations

2.6.1. Calculation of the relative potency factor (RPF)

2.6.2. The calculation of estimated daily intake (EDI)

2.7. Statistical analyses

3. Results and discussion

3.1. Overall detection of NNIs in PM2.5 samples from Wuhan.

3.2. The results of PM2.5 samples stratified by season

3.3. Correlation analysis between the NNIs detected in the PM2.5 samples

3.5. Inhalation exposure in different populations

3.6. Limitations

4. Conclusion

Declarations

Author Contribution

Acknowledgements

References

Additional Declarations

Supplementary Files

Status:

Version 1