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General microbial profile
For the 16S libraries, the six samples recorded 1,054,909 reads, with a length between 51 to 533 bp and an average of 458. In general, the number of clustered sequences was 652,899 (61.89%), while the number of replicated reads was 140,196 (13.29%). The number of classified sequences was 1,047,271 (99.28%) while only 7,155 sequences exhibited ‘unassigned’ (0.68%). The quality control of classification, in this case, the alignment similarity, was between 75 and 100%, while the majority exceeded 80%. Based on the 16S rRNA dataset, prokaryotic OTU identification pipeline,  > 99% of the detected OTUs belonged to the bacteria domain. A total of 1318 species belonging to 17 phyla were detected in all samples. The most abundant were Proteobacteria (75.57%), Bacteroidetes (14.40%), Actinobacteria (0.94%), Verrucomicrobia (0.62%), and Cyanobacteria (0.25%).
For the ITS2 libraries, the six samples recorded 2,193,552 reads, the assembled contigs length between 201 and 482 with an average of 292. In general, the number of generated consensus sequences ranged between seven and 47 per sample. In total, 191 (~ 75%) were successfully identified with pairwise identity ranging from 82 to 100%, while 63 sequences (~ 25%) hit an uncultured species (Supplementary Fig. 1). Based on the customized eukaryote OTU identification pipeline, 118 out of 233 were known species, ~ 55% of the identified OTUs belonged to the kingdom Fungi, ~ 33% belonged to the kingdom Plantae, and ~ 12% belonged to the kingdom Animalia. Due to the high diversity among the detected OTUs, fungi were grouped by their major function rather than their taxonomical position. The most represented Fungi group was the plant pathogens (~ 45%), followed by mushrooms/yeasts (~ 28%), volatile producers (~ 11%), fish pathogens (~ 8%), and human pathogens (~ eight%) of the total fungal OTUs (Fig. 1).
Figure 1

Microbial diversity detected by the metabarcoding analysis. The relative abundance of identified bacterial OTUs among the six water samples, where top abundant bacterial phyla are written in bold (A). The histogram plot shows the identified eukaryotic groups per domain (Planta, Fungi, or Animalia). The target group in the eukaryotic metabarcoding analysis was the fungal group distributed according to their prominent role and function (BBMerge–accurate paired shotgun read merging via overlap).

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Comparative metabarcoding analysis
Microbial diversity indices
For the 16S, the average alpha-diversity was estimated for each source; P-source showed a lower alpha-diversity than I-source. For 16S rRNA, the Simpson index values of the P-sources were lower than the I-sources (Fig. 2). Specifically, in samples from location N compared to the rest of the samples (0.86 for N-I and 0.49 for N-P). For B and G locations, D-index was 0.64 (B-I), 0.54 (B-P), 0.79 (G-I), and 0.66 (G-P). Based on sample locations, beta-diversity values of location G were the highest, while location B was the lowest. The G-I showed the highest beta-diversity for inter-location values, followed by N-P, N-I, G-P, B-I, and B-P (Fig. 2). This might indicate that the changes in a pond diversity are contributed by sources other than the inflow-water (e.g., transferred by juvenile fish or fingerlings, or the introduction of fish feeds).
Figure 2

Alpha and beta-diversity of identified bacterial communities are estimated according to the Simpson diversity index and Bray Curtis, respectively. The three locations (N, G, and B) from two different sources, the inflow- (I) and pond-water (P), are shown.

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Species occurrences and distributions
The identified species were detected in all locations (common) or exclusively detected in (a) either I-source or P-source samples, (b) exclusively found in one location regardless of the water source, (c) uniquely recorded in one sample.
A total of 1318 bacterial species were delimited. In which 1074 species were identified from I-sources, with the highest number was found in B-I (774), followed by N-I (669) and G-I (548). The number of detected species from P-sources was 1006 across the three locations. The highest number of species was found in B-P (882), followed by N-P (553) and G-P (415) locations. The highest number of species was 1081 from location B (321 were unique), followed by 804 species from location N (124 were unique) and 665 species from location G (94 were unique), regardless of the water source (i.e., species detected in one or both samples of each location). Locations N and B had 208 common species, while locations B and G shared 99 species, and locations N and G shared only 19 species. A total of 453 species were common among the three locations, of which 442 were common regardless of the water source.
Out of the 453 common species, six species were exclusively detected from I-sources samples. Among the 442, the highest number of species was 415 from B-I (19 unique), followed by 399 from N-I (five unique) and 383 from G-I (five unique). Forty-one species were common between N-I and B-I, 25 between B-I and G-I, and 22 between N-I and G-I samples. Three hundred thirty-one species were common among samples N-I, B-I, and G-I. The number of the exclusively detected species in P-sources samples was five from the three locations. Among 442 species, the highest number of species was 424 from B-I (26 unique), followed by 386 from N-I (13 unique) and 346 from G-I (five unique). A total of 62 species were common between N-I and B-I, 30 between B-I and G-I, and five between N-I and G-I samples, while 306 were common among N-I, B-I, and G-I samples (Fig. 3).
Figure 3

Venn diagram of shared and uniquely identified OTUs among the three sampling locations (a), where the common OTUs were counted by source (I or P; b). For each source, OTUs were separated by sample locations, respectively (c & d).

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Based on the fungal community, 233 OTUs were detected, 84 were unknown fungi (36%), 31 uncultured fungi with at least one taxonomical rank is known (14%), and 118 species were successfully identified (50%). The detected OTUs in I-sources was 123, with the highest number of OTUs from B-I (79), followed by N-I (37) and G-I (seven). The number of detected OTUs in P-source was 110 from the three locations. The highest number of OTUs was found in B-P (46), followed by N-P (42) and G-P (22) locations. Regardless of the water source, the highest number of OTUs was 125 from location B (24 were unique), followed by 79 OTUs from location N (30 were unique), and 29 OTUs from location G (11 were unique). Locations N and B shared two OTUs, while B and G shared one OTU, and N and G locations shared no OTU. Only one uncultured fungus was shared among the three locations. Between both water sources, based on known and uncultured fungi with at least one taxonomical rank, 20 species were common between the I- and P-sources, 56 species unique for I-sources, and 49 species unique for P-sources. However, none were commonly found among the P-source from the three sample source locations. Fungal OTUs number was following the bacterial OTUs number per sample, reflecting the homogenized overall diversity within each water sample.
Microbial diversity unique to trout aquaculture water
Due to the lack of common eukaryotic OTUs among the P-source sites, the following analysis only focused on the prokaryotic species. The six exclusively identified bacterial species from the I-source belonged to three phyla, Proteobacteria, which has four species (Burkholderiaceae bacterium belong to MWH-UniP1 aquatic group, Caulobacteraceae bacterium, Hyphomonadaceae bacterium, and Rhodospirillales bacterium), one from phylum Bacteroidetes (Spirosomaceae bacterium) and one from phylum Firmicutes (Solibacillus sp.). For the P-source samples, the five exclusively species among the three locations belonged to two phyla, Bacteroidetes (Ekhidna sp., Polaribacter sp., and Sphingobacteriaceae bacterium) and Proteobacteria (Thalassotalea sp. and Paraherbaspirillum sp.).
Among the commonly-shared species, the one-tail distribution student t-test was applied to identify significantly different bacterial species between the two water sources (Table 1). A total of 15 species belonged to two phyla, and 12 families were significantly different between the I- and P-source samples. The phylum Bacteroides (significant at average; p value of 0.001) included eight species: Marinoscillum sp. (Cyclobacteriaceae), Dysgonomonas sp. (Dysgonomonadaceae), Paludibacter sp. (Paludibacteraceae), Saprospiraceae bacterium, Mucilaginibacter and Pedobacter (Sphingobacteriaceae), Lacihabitans (Spirosomaceae), uncultured ST-12K33 (unknown family), and Empedobacter (Weeksellaceae); all of the aforementioned species were less represented in I-source and more represented in P-sources. In the case of the other phylum (Proteobacteria), eight species were found to be significant. Three species: Simplicispira sp. (Burkholderiaceae), Amaricoccus, and Thioclava (Rhodobacteraceae) were up-represented in I-source and while four species: Alicycliphilus and Caenimonas (Burkholderiaceae), Orientia sp. (Rickettsiaceae), and Sphingomonadaceae bacterium were up-represent in P-source.
Table 1 Significantly differentiated bacteria (p  > 0.05) as determined via a t-test, ordered by classification.
Full size table

The species exhibiting the highest overall relative abundance was Simplicispira sp. (0.45), which was up-represented in the I-source, while the uncultured Saprospiraceae bacterium (0.227), Pedobacter sp. (0.164), and uncultured Sphingomonadaceae bacterium (0.213) were up-represented in the P-source.
All data were analyzed using Pearson-based multiple correlation analysis based on the counts of all the identified species and visualized using heatmaps. Samples-based clustering was estimated for several correlation blocks. The single correlation-block that was detected to cluster the samples by location (i.e., N, B, and G) regardless of their source included nine species. Three correlation-blocks were found to cluster the samples by water source (i.e., I or P) regardless of their location, and these included 16 species, one of which included 11 species (Fig. 4). The correlated species were tested for species-species co-occurrence and visualized as a network. One significant connection was formed among five of the 16 species found to distinguish the water source, but none distinguish the sampling location. The detected species belonged to phylum Proteobacteria, Candidatus Symbiobacter sp., Comamonas sp., and Polaromonas sp. (Burkholderiaceae) and Porphyrobacter sp. (Sphingomonadaceae), and one species belonged to phylum Firmicutes, Lachnospiraceae bacterium in one connected cluster. The 11 species that did not form a network were Beijerinckiaceae bacterium, Bacteriap25, Gracilibacter sp., Malikia sp., Oligoflexus sp., Pelomonas sp., Polycyclovorans sp., Thioclava sp., Thauera sp., uncultured Alpha-proteobacterium, and uncultured Gamma-proteobacterium (JTB255; Fig. 4).
Figure 4

source samples from the P-source samples and includes 11 species (A), and the other discriminates the N, B, and G locations regardless of the water source and includes nine species (B).

Heatmaps based on Person-multiple correlation analysis among the identified bacterial species. Two heatmaps, one was able to discriminate the I-

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Influence of samples locations and distance on microbial diversity
The flow of water is northeast; accordingly, the flow of water hypothetically runs first from location N, passes through B, and then finally reaches G. Interestingly, it was observed that both N and B locations shared more OTUs than with the G location (Supplementary Fig. 1). Furthermore, N samples have less OTUs than the B and G sites, which raised a question about the influence of the geographic position and distance on the sampled locations. Based on such a hypothesis, a Euclidean geographic distance matrix was estimated to provide a spatial scale for further correlation analysis. A Mantel test was performed to examine the correlation between the geographic distance between the sampled farms and the number of characteristic species in I- and P-source samples. In the case of the inflow-water samples, no significant (p  > 0.05) correlation was observed. In contrast, the characteristic species count for pond-water samples significantly correlated with the distance between the samples (r = 0.969, p  More

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Global δ15Nw-NO3− observations
Publications of δ15Nw-NO3− studies were obtained through the databases of the Web of Science (http://isiknowledge.com), Google Scholar (http://scholar.google.com.hk), and Baidu Scholar (http://xueshu.baidu.com) by searching keywords of “nitrogen isotope”, “nitrate”, “rainfall”, and “precipitation”. By the end of December 2018, a total of 128 publications were available (Supplementary Text 1), spanning the sampling time of 1956–2017 (Supplementary Fig. 11). We extracted δ15Nw-NO3− values of individual precipitation samples by using the software of Web Plot Digitizer37.
There are totally 3483 individual δ15Nw-NO3− data and 222 sampling sites when multiple observations in different sampling years at the same site were counted once only (Fig. 1). There are 56 urban sites, 158 non-urban sites, and eight arctic sites (Fig. 1), in which non-urban sites are mainly situated in rural, mountain, forest, and lake areas. Due to the sparsity of available data before 2000 (Supplementary Fig. 11), we analyzed δ15Nw-NO3− data at major urban and non-urban sites in East Asia, Europe, and North America during 2000–2017 to ensure a better site representation and to reduce the uncertainty caused by inconsistency in sampling time (Fig. 1). To describe spatial differences in δ15Nw-NO3− values between urban and non-urban sites among three regions (totally 214 sites), only site-based mean values during the period of 2000–2017 (totally 169 sites) were used (detailed in Fig. 2). To describe temporal variations of δ15Nw-NO3− values in urban and non-urban areas of each region, respectively (Fig. 3), we counted observation sites by different sampling years, given that δ15Nw-NO3− observations at few sites have been conducted in different sampling years. In this way, there were a total of 206 sites during 2000–2017 (detailed in Fig. 3). In addition, 35%, 29%, and 36% of the δ15Nw-NO3− observations were conducted in warmer, cooler, and the whole year, respectively. The seasonal effects of NOx emissions may not substantially influence the patterns of regional δ15Nw-NO3− variations.
Differences between δ15Nw-NO3− and δ15Ni-NOx values
NO is normally insoluble in water, and w-NO3− is scavenged only from the ambient NO2 and the oxidized NOx (i.e., HNO3 and p-NO3−) (Supplementary Fig. 1)32,38,39. Moreover, isotopic effects during the NOx cycles lead to differences between δ15NNOx and δ15NNO2. Therefore, substantial differences exist between the δ15Nw-NO3− and δ15Ni-NOx values in the atmosphere (hereafter denoted as 15∆i-NOx→w-NO3−). In this study, we calculated 15∆i-NOx→w-NO3− values by using the following equation (Eq. (2)):

$${,}^{15}{Delta}_{{mathrm{i}} – {mathrm{NO}x} to {mathrm{w}} – {mathrm{NO3}} – } = delta ^{15}{mathrm{N}}_{{mathrm{w}} – {mathrm{NO3}} – } – delta ^{15}{mathrm{N}}_{{mathrm{i}} – {mathrm{NO}x}}.$$
(2)

Combined Eq. (1) with Eq. (2), we get Eq. (3) to calculate the 15∆i-NOx→w-NO3− values.

$$ {,}^{15}{Delta}_{{mathrm{i}} – {mathrm{NO}x} to {mathrm{w}} – {mathrm{NO3}}} = delta ^{15}{mathrm{N}}_{{mathrm{w}} – {mathrm{NO3}} – }\ quad- left({delta}^{15}{mathrm{N}}_{{mathrm{NO}x}} times {mathrm{C}}_{{mathrm{NO2}}}/f_{{mathrm{NO2}}} + delta ^{15}{mathrm{N}}_{{mathrm{HNO3}}} times {mathrm{C}}_{{mathrm{HNO3}}} + delta ^{15}{mathrm{N}}_{{mathrm{p}} – {mathrm{NO3}} – } times {mathrm{C}}_{{mathrm{p}} – {mathrm{NO3}}}right)/\ quad left({mathrm{C}}_{{mathrm{NO2}}}/f_{{mathrm{NO2}}} + {mathrm{C}}_{{mathrm{HNO3}}} + {mathrm{C}}_{{mathrm{p}} – {mathrm{NO3}} – }right).$$
(3)

To obtain more accurate 15∆i-NOx→w-NO3− values, we estimated the 15∆i-NOx→w-NO3− values in two independent scenarios. In Scenario 1, mean values of global δ15NNOx and fNO2 values, simultaneously observed values of ambient CNO2, CHNO3, Cp-NO3−, δ15NHNO3, δ15Np-NO3−, and δ15Nw-NO3− were used for the calculation in Eq. (3). In Scenario 2, non-synchronously observed values of ambient fNO2, CNO2, CHNO3, Cp-NO3−, δ15NNOx, δ15NHNO3, δ15Np-NO3−, and δ15Nw-NO3− were used for the calculation in Eq. (3). The values and data sources of parameters used for estimating ambient 15∆i-NOx→w-NO3− values are included in Supplementary Table 1. Because data of fNO2 and δ15NNOx are very sparse globally, we used global mean values and considered their SD values into the uncertainty analysis by the Monte Carlo method. Furthermore, because of no significant difference between 15∆i-NOx→w-NO3− values obtained in Scenario 1 (2.1 ± 1.7‰) and Scenario 2 (5.7 ± 3.2‰) (Supplementary Fig. 2), we used a mean value of them (3.9 ± 1.8‰; Supplementary Fig. 2) in the calculations of source contributions (Eqs. (4) and (5)).
Contributions of dominant fossil fuel and non-fossil fuel NOx sources
Based on δ15Nw-NO3−, 15∆i-NOx→w-NO3−, and δ15N values of NOx sources, we estimated relative contributions of dominant fossil fuel and non-fossil fuel NOx sources to total NOx emissions by using the isotope mass-balance method. We considered coal combustion (denoted as S1) and vehicle exhausts (S2) as dominant fossil fuel NOx sources, and biomass burning (S3), and microbial N cycles (S4) as dominant non-fossil fuel NOx sources. The major reasons include: (1) these four sources have been considered as dominant sources of total NOx emissions in studies of both emission inventory and deposition modeling2,9,11,13,14,15,19,20,21; (2) they are also the dominant sources influencing δ15N variations of NOx and NO3− in the atmosphere;26,27 (3) their mean δ15N values of NOx emission sources differ significantly (P  More

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