Philippa Kaur

1.
Saunders, S. C., Mislivets, M. R., Chen, J. & Cleland, D. T. Effects of roads on landscape structure within nested ecological units of the Northern Great Lakes Region, USA. Biol. Conserv. 103, 209–225 (2002).
Google Scholar 
2.
Potvin, F., Breton, L. & Courtois, R. Response of beaver, moose, and snowshoe hare to clear-cutting in a Quebec boreal forest: A reassessment 10 years after cut. Can. J. For. Res. 35, 151–160 (2005).
Google Scholar 

3.
Sahlén, E., Støen, O. & Swenson, J. E. Brown bear den site concealment in relation to human activity in Sweden. Ursus 22, 152–158 (2011).
Google Scholar 

4.
James, A. & Stuart-Smith, A. Distribution of caribou and wolves in relation to linear corridors. J. Wildl. Manage. 64, 154–159 (2000).
Google Scholar 

5.
Vitousek, P. M., Mooney, H. A., Lubchenco, J. & Melillo, J. M. Human domination of earth’s ecosystems. Science 277, 494–499 (1997).
CAS  Google Scholar 

6.
Wittmer, H. U., McLellan, B. N., Serrouya, R. & Apps, C. D. Changes in landscape composition influence the decline of a threatened woodland caribou population. J. Anim. Ecol. 76, 568–579 (2007).
PubMed  Google Scholar 

7.
Irwin, L. L., Rock, D. F. & Miller, G. P. Stand structures used by Northern spotted owls in managed forests. J. Raptor Res. 34, 175–186 (2000).
Google Scholar 

8.
Leblond, M., Dussault, C. & Ouellet, J. P. Avoidance of roads by large herbivores and its relation to disturbance intensity. J. Zool. 289, 32–40 (2013).
Google Scholar 

9.
Dickie, M., Serrouya, R., McNay, R. S. & Boutin, S. Faster and farther: Wolf movement on linear features and implications for hunting behaviour. J. Appl. Ecol. 54, 253–263 (2017).
Google Scholar 

10.
Finnegan, L. et al. Natural regeneration on seismic lines influences movement behaviour of wolves and grizzly bears. PLoS ONE https://doi.org/10.1371/journal.pone.0195480 (2018).
Article  PubMed  PubMed Central  Google Scholar 

11.
Whittington, J. et al. Caribou encounters with wolves increase near roads and trails: A time-to-event approach. J. Appl. Ecol. 48, 1535–1542 (2011).
Google Scholar 

12.
Sorensen, T. et al. Determining sustainable levels of cumulative effects for boreal caribou. J. Wildl. Manage. 72, 900–905 (2008).
Google Scholar 

13.
Dabros, A., Pyper, M. & Castilla, G. Seismic lines in the boreal and arctic ecoystems of North America: Environmental impacts, challenges and opportunities. Environ. Rev. 26, 214–229 (2018).
Google Scholar 

14.
Lee, P. & Boutin, S. Persistence and developmental transition of wide seismic lines in the western Boreal Plains of Canada. J. Environ. Manage. 78, 240–250 (2006).
PubMed  Google Scholar 

15.
Pigeon, K. E. et al. Toward the restoration of caribou habitat: Understanding factors associated with human motorized use of legacy seismic lines. Environ. Manage. 58, 821–832 (2016).
ADS  PubMed  Google Scholar 

16.
Schneider, R. R., Hauer, G., Adamowicz, W. L. V. & Boutin, S. Triage for conserving populations of threatened species: The case of woodland caribou in Alberta. Biol. Conserv. 143, 1603–1611 (2010).
Google Scholar 

17.
Environment Canada. Recovery strategy for the woodland Caribou (Rangifer tarandus caribou), boreal population, in Canada. in Species at Risk Act Recovery Strategy Series 138 (Environment Canada, 2012).

18.
Environment Canada. Recovery strategy for the woodland Caribou, southern mountain population (Rangifer tarandus caribou) in Canada. in Species at Risk Act Recovery Strategy Series. Environment 103 (Environment Canada, Ottawa, 2014).

19.
Dickie, M., Serrouya, R., DeMars, C., Cranston, J. & Boutin, S. Evaluating functional recovery of habitat for threatened woodland caribou. Ecosphere 8, e01936. https://doi.org/10.1002/ecs2.1936 (2017).
Article  Google Scholar 

20.
DeMars, C. A. & Boutin, S. Nowhere to hide: Effects of linear features on predator-prey dynamics in a large mammal system. J. Anim. Ecol. 87, 274–284 (2018).
PubMed  Google Scholar 

21.
Johnson, C. J., Ehlers, L. P. W. & Seip, D. R. Witnessing extinction—Cumulative impacts across landscapes and the future loss of an evolutionarily significant unit of woodland caribou in Canada. Biol. Conserv. 186, 176–186 (2015).
Google Scholar 

22.
Fisher, J. T. & Burton, A. C. Widlife winners and losers in an oil sands landscape. Front. Ecol. Environ. 16, 323–328 (2018).
Google Scholar 

23.
Ehlers, L. P. W., Johnson, C. J. & Seip, D. R. Evaluating the influence of anthropogenic landscape change on Wolf distribution: Implications for woodland caribou. Ecosphere 7, e01600. https://doi.org/10.1002/ecs2.1600 (2016).
Article  Google Scholar 

24.
Houle, M., Fortin, D., Dussault, C., Courtois, R. & Ouellet, J.-P. Cumulative effects of forestry on habitat use by gray wolf (Canis lupus) in the boreal forest. Landscape. Ecol. 25, 419–433 (2010).
Google Scholar 

25.
Mysterud, A. & Ims, R. A. Functional responses in habitat use: Availability influences relative use in trade-off situations. Ecology 79, 1435–1441 (1998).
Google Scholar 

26.
Lima, S. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–639 (1990).
Google Scholar 

27.
Hebblewhite, M., Merrill, E. H. & McDonald, T. L. Spatial decomposition of predation risk using resource selection functions: An example in a wolf-elk predator-prey system. Oikos 111, 101–111 (2005).
Google Scholar 

28.
Latham, A. D. M., Latham, M. C., Boyce, M. & Boutin, S. Movement responses by wolves to industrial linear features and their effect on woodland caribou in northeastern Alberta. Ecol. Appl. 21, 2854–2865 (2011).
Google Scholar 

29.
Visscher, D. R. & Merrill, E. H. Temporal dynamics of forage succession for elk at two scale: Implications of forest management. For. Ecol. Manage. 257, 96–106 (2009).
Google Scholar 

30.
McLoughlin, P., Dunford, J. & Boutin, S. Relating predation mortality to broad-scale habitat selection. J. Anim. Ecol. 74, 701–707 (2005).
Google Scholar 

31.
Ausband, D. E. et al. Surveying predicted rendezvous sites to monitor gray wolf populations. J. Wildlife. Manage. 71, 1043–1049 (2010).
Google Scholar 

32.
Corns, I. & Annas, R. M. Field Guide to Forest Ecosystems of West-Central Alberta 251 (Canadian Forest Service Northern Forestry Centre, Edmonton, 1986).
Google Scholar 

33.
van Rensen, C. K., Nielsen, S. E., White, B., Vinge, T. & Lieffers, V. J. Natural regeneration of forest vegetation on legacy seismic lines in boreal habitats in Alberta’s oil sands region. Biol. Conserv. 184, 127–135 (2015).
Google Scholar 

34.
Swanson, M. E. et al. The forgotten stage of forest succession: Early-successional ecoystems on forest sites. Front. Ecol. Environ. 9, 117–125 (2010).
Google Scholar 

35.
Melin, M., Matala, J., Mehtätalo, L., Pusenius, J. & Packalen, P. Ecological dimensions of airborne laser scanning—Analyzing the role of forest structure in moose habitat use within a year. Remote Sens. Environ. 173, 238–247 (2015).
ADS  Google Scholar 

36.
Roffler, G. H., Gregovich, D. P. & Larson, K. R. Resource selection by coastal wolves reveals the seasonal importance of seral forest and suitable prey habitat. For. Ecol. Manage. 409, 190–201 (2018).
Google Scholar 

37.
DeCesare, N. J. et al. Transcending scale dependence in identifying habitat with resource selection functions. Ecology 22, 1068–1083 (2012).
Google Scholar 

38.
Neufeld, L. M. Spatial Dynamics of Wolves and Woodland Caribou in an Industrial Forest Landscape in West-Central Alberta 155 (University of Alberta, Alberta, 2006).
Google Scholar 

39.
Webb, N., Hebblewhite, M. & Merrill, E. Statistical methods for identifying wolf kill sites using global positioning system locations. J. Wildl. Manage. 72, 1798–1804 (2008).
Google Scholar 

40.
Jedrzejewski, W., Schmidt, K., Theuerkauf, J., Jedrzejewska, B. & Okarma, H. Daily movements and territory use by radio-collared wolves (Canis lupus) in Bialowieza primeval forest in Poland. Can. J. Zool. 79, 1993–2004 (2001).
Google Scholar 

41.
Mech, L. D. & Boitani, L. Wolves 472 (University of Chicago Press, Chicago, Behaviour, Ecology and Conservation, 2003).
Google Scholar 

42.
Jenness, J. Topographic position index (tpi_jen.avx) extension for ArcView 3.x v. 1.3a https://www.jennessent.com/arcview/tpi.htm (2006). Accessed 15 June 2014.

43.
Gessler, P. E., Chadwick, O. A., Chamran, F., Althouse, L. & Holmes, K. Modeling soil–landscape and ecosystem properties using terrain attributes. Soil Sci. Soc. Am. J. 64, 2046 (2000).
ADS  CAS  Google Scholar 

44.
Franklin, S. E., Peddle, D. R. & Dechka, J. A. Evidential reasoning with Landsat TM, DEM and GIS data for landcover classification in support of grizzly bear habitat mapping. Int. J. Remote Sens. 23, 4633–4652 (2002).
ADS  Google Scholar 

45.
McDermid, G. J. et al. Remote sensing and forest inventory for wildlife habitat assessment. For. Ecol. Manage. 257, 2262–2269 (2009).
Google Scholar 

46.
Environmental Systems Research Institute [ESRI] ArcGIS Desktop: Release 10. Redlands, California, (2015).

47.
MacNearney, D. et al. Heading for the hills? Evaluating spatial distribution of woodland caribou in response to a growing anthropogenic disturbance footprint. Ecol. Evol. 6, 6484–6509 (2016).
PubMed  PubMed Central  Google Scholar 

48.
Nielsen, S. E., Cranston, J., Stenhouse, G. B. & Street, M. Identification of priority areas for grizzly bear conservation and recovery in Alberta, Canada. J. Conserv. Plan. 5, 38–60 (2009).
Google Scholar 

49.
White, B. et al. Using the cartographic depth-to-water index to locate small streams and associated wet areas across landscapes. Can. Water Resour. J. 37, 333–347 (2012).
Google Scholar 

50.
Canadell, J. et al. Maximum rooting depth of vegetation types at the global scale. Oecologia 108, 583–595 (1996).
ADS  CAS  PubMed  Google Scholar 

51.
Beyer, H. Geospatial Modelling Environment (version 0.7.2.1) https://www.spatialecology.com/gme (2012). Accessed 16 April 2016.

52.
Murtaugh, P. Simplicity and complexity in ecological data analysis. Ecology 88, 56–62 (2007).
PubMed  Google Scholar 

53.
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inferences: A Practical Information-Theoretic Approach 2nd edn. (Springer, New Yirk, 2002).
Google Scholar 

54.
Takahata, C., Nielsen, S. E., Takii, A. & Izumiyama, S. Habitat selection of a large carnivore along human-wildlife boundaries in a highly modified landscape. PLoS ONE 9, e86181. https://doi.org/10.1371/journal.pone.0086181 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

55.
Fieberg, J., Matthiopoulos, J., Hebblewhite, M., Boyce, M. & Frair, J. Correlation and studies of habitat selection: Problem, red herring, or opportunity?. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 2233–2244 (2010).
PubMed  PubMed Central  Google Scholar 

56.
Muff, S., Signer, J. & Fieberg, J. Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation. J. Anim. Ecol. 89, 80–92 (2020).
PubMed  Google Scholar 

57.
Fieberg, J., Rieger, R. H., Zicus, M. C. & Schildcrout, J. S. Regression modelling of correlated data in ecology: Subject-specific and population averaged response patterns. J. Appl. Ecol. 46, 1018–1025 (2009).
Google Scholar 

58.
Glenn, E. M., Hansen, M. C. & Anthony, R. G. Spotted owl home-range and habitat use in young forests of western Oregon. J. Wildl. Manage. 68, 33–50 (2004).
Google Scholar 

59.
Sawyer, H., Nielson, R. M., Lindzey, F. & McDonald, L. L. Winter habitat selection of mule deer before and during development of a natural gas field. J. Wildl. Manage. 70, 396–403 (2006).
Google Scholar 

60.
Manly, B. F. J., McDonald, L. L., Thomas, D. L., McDonald, T. L. & Erickson, W. P. Resource Selection by Animals—Statistical Design and Analysis for Field Studies 2nd edn. (Kluwer Acadamic Publishers, Berlin, 2002).
Google Scholar 

61.
Hebblewhite, M., Percy, M. & Merrill, E. H. Are all global positioning system collars created equal? Correcting habitat-induced bias using three brands in the central Canadian Rockies. J. Wildl. Manage. 71, 2026–2033 (2007).
Google Scholar 

62.
Frair, J. L. et al. Removing GPS collar bias in habitat selection studies. J. Appl. Ecol. 41, 201–212 (2004).
Google Scholar 

63.
Lumley, T. Survey: Analysis of complex survey samples. R packages version 3.30 (2014).

64.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2015). Accessed 12 Dec 2016.

65.
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
Google Scholar 

66.
Matthiopoulos, J., Hebblewhite, M., Aarts, G. & Fieberg, J. Generalized functional responses for species distributions. Ecology 92, 583–589 (2011).
PubMed  Google Scholar 

67.
McKenzie, H. W., Merrill, E. H., Spiteri, R. J. & Lewis, M. A. How linear features alter predator movement and the functional response. Interface Focus. 2, 205–216 (2012).
PubMed  PubMed Central  Google Scholar 

68.
Droghini, A. & Boutin, S. Snow conditions influence grey wolf (Canis lupus) travel paths: The effect of human-created linear features. Can. J. Zool. 96, 39–47 (2017).
Google Scholar 

69.
García-Marmolejo, G., Chapa-Vargas, L., Weber, M. & Huber-Sannwald, E. Landscape composition influences abundance patterns and habitat use of three ungulate species in fragmented secondary deciduous tropical forests, Mexico. Glob. Ecol. Conserv. 3, 744–755 (2015).
Google Scholar 

70.
DeCesare, N. J. Separating spatial search and efficiency rates as components of predation risk. Proc. R. Soc. B 279, 4626–4633 (2012).
PubMed  Google Scholar  More

Surface water frequency maps and surface water areas during 1989–2016
Surface water frequencies (FW) of individual pixels in 2016 varied substantially across China (Fig. 1a). There were 1444 million pixels with annual surface water frequency of FW  > 0 in 2016, amounting to ~1.3 × 106 km2 maximum SWA in 2016. Based on the surface water frequency in a year, a water pixel was defined as year-long surface water (FW ≥ 0.75), seasonal surface water (0.05 ≤ FW  More

1.
Rezapour, S., Atashpaz, B., Moghaddam, S. S. & Damalas, C. A. Heavy metal bioavailability and accumulation in winter wheat (Triticum aestivum L.) irrigated with treated wastewater in calcareous soils. Sci. Total Environ. 656, 261–269. https://doi.org/10.1016/j.scitotenv.2018.11.288 (2019).
ADS  CAS  Article  PubMed  Google Scholar 
2.
Wang, S., Wu, W., Liu, F., Liao, R. & Hu, Y. Accumulation of heavy metals in soil–crop systems: a review for wheat and corn. Environ. Sci. Pollut. Res. 24, 15209–15225. https://doi.org/10.1007/s11356-017-8909-5 (2017).
CAS  Article  Google Scholar 

3.
Rezapour, S., Kouhinezhad, P., Samadi, A. & Rezapour, M. Level, pattern, and risk assessment of the selected soil trace metals in the calcareous cultivated Vertisols. Chem. Ecol. 8, 692–706. https://doi.org/10.1080/02757540.2013.810728 (2015).
CAS  Article  Google Scholar 

4.
Zhang, Y. et al. Heavy metal accumulation and health risk assessment in soil-wheat system under different nitrogen levels. Sci. Total Environ. 622–623, 1499–1508. https://doi.org/10.1016/j.scitotenv.2017.09.317 (2018).
ADS  CAS  Article  PubMed  Google Scholar 

5.
Gill, R. A. et al. Reduced glutathione mediates pheno-ultrastructure kinome and transportome in chromium-induced Brassica napus L.. Front. Plant Sci. 8, 2037. https://doi.org/10.3389/fpls.2017.02037 (2017).
Article  PubMed  PubMed Central  Google Scholar 

6.
Khan, M. U., Malik, R. N. & Muhammad, S. Human health risk from heavy metal via food crops consumption with wastewater irrigation practices in Pakistan. Chemosphere 93, 2230–2238. https://doi.org/10.1016/j.chemosphere.2013.07.067 (2013).
ADS  CAS  Article  PubMed  Google Scholar 

7.
Zajac, L. et al. Probabilistic estimates of prenatal lead exposure at toxic hotspots in low- and middle-income countries. Environ. Res. 183, 109251. https://doi.org/10.1016/j.envres.2020.109251 (2020).
CAS  Article  PubMed  Google Scholar 

8.
Odongo, A. O., Moturi, W. N. & Mbuthia, E. K. Heavy metals and parasitic geo helminths toxicity among geophagous pregnant women: a case study of Nakuru Municipality, Kenya. Environ. Geochem. Health 38, 123–131. https://doi.org/10.1007/s10653-015-9690-3 (2015).
CAS  Article  PubMed  Google Scholar 

9.
Vergara, C., María, C., Judith, L. P. & Rodriguez, H. Effects of co-cropping on soybean growth and stress response in lead-polluted soils. Chemosphere 246, 125833. https://doi.org/10.1016/j.chemosphere.2020.125833 (2020).
ADS  CAS  Article  Google Scholar 

10.
Shekar, C. C., Sammaiah, D., Shasthree, T. & Reddy, K. J. Effect of mercury on tomato growth and yield attributes. Int. J. Pharm. Biol. Sci. 2, B358–B364. https://doi.org/10.1007/s11356-018-1498-0 (2011).
CAS  Article  Google Scholar 

11.
Tiwari, K., Singh, N. K. & Rai, U. N. Chromium phytotoxicity in radish (Raphanus sativus): effects on metabolism and nutrient uptake. Bull. Environ. Contam. Toxicol. 91, 339–344. https://doi.org/10.1007/s00128-013-1047-y (2013).
CAS  Article  PubMed  Google Scholar 

12.
Bergqvist, C., Herbert, R., Persson, I. & Greger, M. Plants influence on arsenic availability and speciation in the rhizosphere, roots and shoots of three different vegetables. Environ. Pollut. 184, 540–546. https://doi.org/10.1016/j.envpol.2013.10.003 (2014).
CAS  Article  PubMed  Google Scholar 

13.
Rizwan, M. et al. A critical review on effects, tolerance mechanisms and management of cadmium in vegetables. Chemosphere 182, 90–105. https://doi.org/10.1016/j.chemosphere.2017.05.013 (2017).
ADS  CAS  Article  PubMed  Google Scholar 

14.
Rafaqat, A. G. et al. Chromium-induced physio-chemical and ultrastructural changes in four cultivars of Brassica napus L.. Chemosphere 120, 154–164. https://doi.org/10.1016/j.chemosphere.2014.06.029 (2015).
ADS  CAS  Article  Google Scholar 

15.
Ali, B. et al. Regulation of cadmium-induced proteomic and metabolic changes by 5-aminolevulinic acid in leaves of Brassica napus L.. PLoS ONE 10(4), e0123328. https://doi.org/10.1371/journal.pone.0123328 (2015) (eCollection).
CAS  Article  PubMed  PubMed Central  Google Scholar 

16.
Basharat, A. et al. Promotive role of 5-aminolevulinic acid on mineral nutrients and antioxidative defense system under lead toxicity in Brassica napus. Ind. Crops Prod. 52, 617–626. https://doi.org/10.1016/j.indcrop.2013.11.033 (2014).
CAS  Article  Google Scholar 

17.
Gill, R. A. et al. Genotypic variation of the responses to chromium toxicity in four oilseed rape cultivars. Biol. Plant. 58, 539–550. https://doi.org/10.1007/s10535-014-0430-9 (2014).
CAS  Article  Google Scholar 

18.
Tandon, V., Gupta, B. M. & Tandon, R. Free radicals/reactive oxygen species. JK Pract. Nurs. Res. Pract. 12, 143–148. https://doi.org/10.1155/2011/260482 (2005).
Article  Google Scholar 

19.
Yang, Y., Liu, H., Xiang, X. H. & Liu, F. Y. Outline of occupational chromium poisoning in China. Bull Environ. Contam. Toxicol. 90, 742–749. https://doi.org/10.1007/s00128-013-0998-3 (2013).
CAS  Article  PubMed  Google Scholar 

20.
Vaiserman, A. M. Aging-modulating treatments: from reductionism to a system oriented perspective. Front. Genet. 5, 1–3. https://doi.org/10.3389/fgene.2014.00446 (2016).
CAS  Article  Google Scholar 

21.
Bewley, J.D. & Black, M. Biochemistry of germination and growth. In: Physiology and Biochemistry of seeds in relation to germination. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-66668-1_5 (1978).

22.
Zadoks, J. C., Chang, T. T. & Konzak, C. T. A decimal code for the growth stages of cereals. Weed Res. 14, 415–421. https://doi.org/10.1111/j.1365-3180.1974.tb01084.x (1974).
Article  Google Scholar 

23.
Vernay, P. et al. Effect of chromium species on phytochemical and physiological parameters in Datura innoxia. Chemosphere 72, 763–771. https://doi.org/10.1016/j.chemosphere.2008.03.018 (2008).
ADS  CAS  Article  PubMed  Google Scholar 

24.
Arnon, D. T. Copper enzymes in isolated chloroplasts: polyphenol oxidase in Beta vulgaris. J. Plant Physiol. 24, 1–15. https://doi.org/10.1104/pp.24.1.1 (1949).
CAS  Article  Google Scholar 

25.
Zofia, L., Kmiecik, W. & Korus, A. Content of vitamin C, carotinoids, chlorophylls and polyphenols in green parts of dill (Anethum graveolens L.) depending on plant height. J. Food Compos. Anal. 19, 134–140. https://doi.org/10.1016/j.jfca.2005.04.009 (2006).
CAS  Article  Google Scholar 

26.
Ryan, J., Estfan, G. & Rashid, A. Soil and Plant Analysis Laboratory Manual. 2nd ed., pp. 87–89. ISBN 9788172337650 (2001).

27.
Panichev, N., Mandiwana, K., Kataeva, M. & Siebert, S. Determination of Cr (VI) in plants by electrothermal atomic absorption spectrometry after leaching with sodium carbonate. Spectrochim. Acta Part B 60, 699–703. https://doi.org/10.1016/j.sab.2005.02.018 (2005).
ADS  CAS  Article  Google Scholar 

28.
Chandra, R., Kumar, P. K. & Singh, J. Impact of an aerobically treated and untreated (raw) distillery effluent irrigation on soil micro flora, growth, total chlorophyll and protein contents of Phaseolus aureus L.. J. Environ. Biol. 25, 381–385 (2004).
PubMed  Google Scholar 

29.
Velthof, G., Van-Beusichem, M. & Raijmakers, W. Relationship between availability indices and plant uptake of nitrogen and phosphorus from organic products. Plant Soil 200, 215. https://doi.org/10.1023/A:1004336903214 (1998).
CAS  Article  Google Scholar 

30.
Steel, R. G. D. & Torrie, J. H. Principles and Procedures of Statistics 172–177 (McGraw Hill Book Crop., Inc., Singapore, 1984).
Google Scholar 

31.
Chun, X. L. et al. Effects of arsenic on seed germination and physiological activities of wheat seedlings. J. Environ. Sci. 19, 725–732. https://doi.org/10.1016/S1001-0742(07)60121-1 (2007).
Article  Google Scholar 

32.
Alghobar, M. A. & Suresha, A. Evaluation of metal accumulation in soil and tomatoes irrigated with sewage water from Mysore city, Karnataka India. J. Saudi Soc. Agric. Sci. 16, 49–59. https://doi.org/10.1016/j.jssas.2015.02.002 (2017).
Article  Google Scholar 

33.
Yourtchi, M. S. & Bayat, H. Y. Effect of cadmium toxicity on growth, cadmium accumulation and macronutrient content of durum wheat (Dena CV). Int. J. Agric. Crop Sci. 6, 1099–1103 (2013).
CAS  Google Scholar 

34.
Barberon, M. & Geldner, N. Radial transport of nutrients: the plant root as a polarized epithelium. Plant Physiol. 166, 528–537. https://doi.org/10.1104/pp.114.246124 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

35.
Shahid, M. et al. Heavy-metal-induced reactive oxygen species: phytotoxicity and physicochemical changes in plants. Rev. Environ. Contam. Toxicol. 232, 1–44. https://doi.org/10.1007/978-3-319-06746-9_1 (2014).
CAS  Article  PubMed  Google Scholar 

36.
Yadav, K. K. et al. Mechanistic understanding and holistic approach of phytoremediation: a review on application and future prospects. Ecol. Eng. 120, 274–298. https://doi.org/10.1016/j.ecoleng.2018.05.039 (2018).
Article  Google Scholar 

37.
Gopal, R. & Rizvi, A. H. Excess lead alters growth, metabolism and translocation of certain nutrients in radish. Chemosphere 70, 1539–1544. https://doi.org/10.1016/j.chemosphere.2007.08.043 (2008).
ADS  CAS  Article  PubMed  Google Scholar 

38.
Antoniadis, V. et al. Trace elements in the soil-plant interface: phytoavailability, translocation, and phytoremediation—a review. Earth Sci. Rev. 172, 621–645. https://doi.org/10.1016/j.earscirev.2017.06.005 (2017).
ADS  CAS  Article  Google Scholar 

39.
Hamid, N., Bukhari, N. & Jawaid, F. Physiological responses of phaseolus vulgaris to different lead concentrations. Pak. J. Bot. 42, 239–246 (2010).
CAS  Google Scholar 

40.
Osma, M., Serin, Z. & Leblebici, A. Heavy metals accumulation in some vegetables and soils in Istanbul. Ekoloji. 21, 1–8. https://doi.org/10.5053/ekoloji.2011.821 (2012).
CAS  Article  Google Scholar 

41.
Singh, S., Parihar, P., Singh, R., Singh, V. P. & Prasad, S. M. Heavy metal tolerance in plants: role of transcriptomics, proteomics, metabolomics, and ionomics. Front. Plant Sci. 6, 1143–1148. https://doi.org/10.3389/fpls.2015.01143 (2015).
Article  PubMed  Google Scholar 

42.
Zeng, L. S., Liao, M., Chen, C. L. & Huang, C. Y. Effects of lead contamination on soil microbial activity and physiological indices in soil-Pb-rice (Oryza sativa L.) system. Chemosphere 65, 567–574. https://doi.org/10.1016/j.chemosphere.2006.02.039 (2006).
ADS  CAS  Article  PubMed  Google Scholar 

43.
Qadir, S., Qureshi, M. I., Javed, S. & Abdin, M. Z. Genotypic variation in phytoremediation potential of Brassica juncea cultivars exposed to Cd stress. Plant Sci. 167, 1171–1181. https://doi.org/10.1016/j.plantsci.2004.06.018 (2004).
CAS  Article  Google Scholar 

44.
Xiong, T. T. et al. Foliar uptake and metal(loid) bioaccessibility in vegetables exposed to particulate matter. Environ. Geochem. Health 36, 897–909. https://doi.org/10.1007/s10653-014-9607-6 (2014).
CAS  Article  PubMed  Google Scholar 

45.
Zheljazkov, V. D. & Nielsen, N. E. Effect of heavy metals on peppermint and cornmint. Plant Soil 178, 59–66. https://doi.org/10.1007/BF00011163 (1996).
CAS  Article  Google Scholar 

46.
Lavado, R. S., Porcelli, C. A. & Alvarez, R. Nutrient and heavy metal concentration and distribution in corn, soybean and wheat as affected by different tillage systems in Argentine Pampas. Soil Tillage Res. 62, 55–60. https://doi.org/10.1016/S0167-1987(01)00216-1 (2001).
Article  Google Scholar 

47.
Gupta, N. et al. Trace elements in soil-vegetables interface: translocation, bioaccumulation, toxicity and amelioration—a review. Sci. Total Environ. 651, 2927–2942. https://doi.org/10.1016/j.scitotenv.2018.10.047 (2019).
ADS  CAS  Article  PubMed  Google Scholar 

48.
Ho, W. M., Ang, L. H. & Lee, D. K. Assessment of Pb uptake, translocation in Kenaf (Hibiscus cannabinus L.) for phytoremediation of sand tailings. J. Evniron. Sci. 20, 1341–47. https://doi.org/10.1016/S1001-0742(08)62231-7 (2008).
CAS  Article  Google Scholar 

49.
Vogel-Mikus, K., Drobne, D. & Regvar, M. Zn, Cd and Pb accumulation and arbuscular mycorrhizal colonization of pennycress Thlaspi praecox Wulf (Brassicaceae) from the vicinity of a lead mine and smelter in Slovenia. Environ. Pollut. 133, 233–242. https://doi.org/10.1016/j.envpol.2004.06.021 (2005).
CAS  Article  PubMed  Google Scholar 

50.
Liu, J. G., Li, K. Q., Xu, J. K. & Zhang, Z. J. Lead toxicity, uptake and translocation in different rice cultivars. Plant Sci. 165, 793–802. https://doi.org/10.1016/S0168-9452(03)00273-5 (2003).
CAS  Article  Google Scholar 

51.
Zhang, M. K., Liu, Z. Y. & Wang, H. Use of single extraction methods to predict bioavailability of heavy metals in polluted soils to rice. Commun. Soil Sci. Plant Anal. 41, 820–831. https://doi.org/10.1080/00103621003592341 (2010).
CAS  Article  Google Scholar 

52.
Shahid, M. et al. Foliar heavy metal uptake, toxicity and detoxification in plants: a comparison of foliar and root metal uptake. J. Hazard. Mater. 325, 36–58. https://doi.org/10.1016/j.jhazmat.2016.11.063 (2016).
CAS  Article  PubMed  Google Scholar 

53.
Sharma, R. K., Agrawal, M., Bhushan, S. & Agrawal, S. B. Physiological and biochemical responses resulting from cadmium and zinc accumulation in carrot plants. J. Plant Nutr. 33, 1066–1079. https://doi.org/10.1080/01904161003729774 (2010).
CAS  Article  Google Scholar 

54.
McBride, M. B., Shayler, H. A., Russell-Anelli, J. M., Spliethoff, H. M. & Marquez, L. G. Arsenic and lead uptake by vegetable crops grown on an old Orchard site amended with compost. Water Air Soil Pollut. 226, 265–272. https://doi.org/10.1007/s11270-015-2529-9 (2015).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar  More

1.
Ward, J. V & Stanford, J. A. Serial discontinuity concept of lotic ecosystems. In Dynamics of Lotic Systems, Ann Arbor Science, Ann Arbor 29–42 (1983).
2.
Bouwman, A. F. et al. Nutrient dynamics, transfer and retention along the aquatic continuum from land to ocean: towards integration of ecological and biogeochemical models. Biogeosciences 10, 1–22 (2013).
ADS  Google Scholar 

3.
Proia, L. et al. Microbial carbon processing along a river discontinuum. Freshw. Sci. 35, 1133–1147 (2016).
Google Scholar 

4.
Casas-Ruiz, J. P. et al. A tale of pipes and reactors: Controls on the in-stream dynamics of dissolved organic matter in rivers. Limnol. Oceanogr. 62, S85–S94 (2017).
CAS  Google Scholar 

5.
Artigas, J. et al. Phosphorus use by planktonic communities in a large regulated Mediterranean river. Sci. Total Environ. 426, 180–187 (2012).
ADS  CAS  PubMed  Google Scholar 

6.
Gómez-Gener, L., Gubau, M., von Schiller, D., Marcé, R. & Obrador, B. Effect of small water retention structures on diffusive CO2 and CH4 emissions along a highly impounded river. Inl. Waters 8, 449–460 (2018).
Google Scholar 

7.
Irriberri, J., Unanue, M., Barcina, I. & Egea, L. Seasonal variation in population density and heterotrophic activity of attached and free-living bacteria in coastal waters. Appl. Environ. Microbiol. 53, 2308–2314 (1987).
Google Scholar 

8.
Grossart, H. & Simon, M. Bacterial colonization and microbial decomposition of limnetic organic aggregates (lake snow). Aquat. Microb. Ecol. 15, 1127–1140 (1998).
Google Scholar 

9.
Simon, M., Grossart, H. P., Schweitzer, B. & Ploug, H. Microbial ecology of organic aggregates in aquatic ecosystems. Aquat. Microb. Ecol. 28, 175–211 (2002).
Google Scholar 

10.
Wilczek, S., Wörner, U., Pusch, M. T. & Fischer, H. Role of suspended particles for extracellular enzyme activity and biotic control of pelagic bacterial populations in the large lowland river Elbe. Fundam. Appl. Limnol./Arch. für Hydrobiol. 169, 153–168 (2007).
Google Scholar 

11.
Rieck, A., Herlemann, D. P. R., Jürgens, K. & Grossart, H. P. Particle-associated differ from free-living bacteria in surface waters of the baltic sea. Front. Microbiol. 6, 1297 (2015).
PubMed  PubMed Central  Google Scholar 

12.
Ruiz-González, C., Proia, L., Ferrera, I., Gasol, J. M. & Sabater, S. Effects of large river dam regulation on bacterioplankton community structure. FEMS Microbiol. Ecol. 84, 316–331 (2013).
PubMed  Google Scholar 

13.
Salazar, G. et al. Particle-association lifestyle is a phylogenetically conserved trait in bathypelagic prokaryotes. Mol. Ecol. 24, 5692–5706 (2015).
PubMed  Google Scholar 

14.
López-Pérez, M., Kimes, N. E., Haro-Moreno, J. M. & Rodriguez-Valera, F. Not all particles are equal: The selective enrichment of particle-associated bacteria from the mediterranean sea. Front. Microbiol. 7, 996 (2016).
PubMed  PubMed Central  Google Scholar 

15.
Mestre, M., Borrull, E., Sala, M. & Gasol, J. M. Patterns of bacterial diversity in the marine planktonic particulate matter continuum. ISME J. 11, 999–1010 (2017).
PubMed  PubMed Central  Google Scholar 

16.
Zeglin, L. H. Stream microbial diversity in response to environmental changes: Review and synthesis of existing research. Front. Microbiol. 6, 1–15 (2015).
Google Scholar 

17.
Galand, P. E., Lovejoy, C., Pouliot, J. & Vincent, W. F. Heterogeneous archaeal communities in the particle-rich environment of an arctic shelf ecosystem. J. Mar. Syst. 74, 774–782 (2008).
Google Scholar 

18.
Orsi, W. D. et al. Ecophysiology of uncultivated marine euryarchaea is linked to particulate organic matter. ISME J. 9, 1747–1763 (2015).
PubMed  PubMed Central  Google Scholar 

19.
Crump, B. C. & Baross, J. A. Archaeaplankton in the Columbia River, its estuary and the adjacent coastal ocean, USA. FEMS Microbiol. Ecol. 31, 231–239 (2000).
CAS  PubMed  Google Scholar 

20.
Dumestre, J., Casamayor, E. O., Massana, R. & Pedrós-alió, C. Changes in bacterial and archaeal assemblages in an equatorial river induced by the water eutrophication of Petit Saut dam reservoir (French Guiana). Aquat. Microb. Ecol. 26, 209–221 (2001).
Google Scholar 

21.
Galand, P. E., Lovejoy, C. & Vincent, W. F. Remarkably diverse and contrasting archaeal communities in a large arctic river and the coastal Arctic Ocean. Aquat. Microb. Ecol. 44, 115–126 (2006).
Google Scholar 

22.
Leibold, M. A. & Chase, J. M. Metacommunity Ecology (Princeton University Press, Princeton, 2018).
Google Scholar 

23.
Lindström, E. S. & Langenheder, S. Local and regional factors influencing bacterial community assembly. Environ. Microbiol. Rep. 4, 1–9 (2012).
PubMed  Google Scholar 

24.
Staley, C. et al. Species sorting and seasonal dynamics primarily shape bacterial communities in the Upper Mississippi River. Sci. Total Environ. 505, 435–445 (2015).
ADS  CAS  PubMed  Google Scholar 

25.
Ruiz-González, C. et al. Weak coherence in abundance patterns between bacterial classes and their constituent OTUs along a regulated river. Front. Microbiol. 6, 1–13 (2015).
Google Scholar 

26.
Grossart, H. P. Ecological consequences of bacterioplankton lifestyles: Changes in concepts are needed. Environ. Microbiol. Rep. 2, 706–714 (2010).
PubMed  Google Scholar 

27.
Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nat. Rev. Microbiol. 5, 782–791 (2007).
CAS  PubMed  Google Scholar 

28.
Böckelmann, U., Manz, W., Neu, T. R. & Szewzyk, U. Characterization of the microbial community of lotic organic aggregates (‘river snow’) in the Elbe River of Germany by cultivation and molecular methods. FEMS Microbiol. Ecol. 33, 157–170 (2000).
Google Scholar 

29.
Ghiglione, J. F. et al. Diel and seasonal variations in abundance, activity, and community structure of particle-attached and free-living bacteria in NW Mediterranean Sea. Microb. Ecol. 54, 217–231 (2007).
CAS  PubMed  PubMed Central  Google Scholar 

30.
Rösel, S. & Grossart, H. P. Contrasting dynamics in activity and community composition of free-living and particle-associated bacteria in spring. Aquat. Microb. Ecol. 66, 169–181 (2012).
Google Scholar 

31.
Crespo, B. G., Pommier, T., Fernández-Gómez, B. & Pedrós-Alió, C. Taxonomic composition of the particle-attached and free-living bacterial assemblages in the Northwest Mediterranean Sea analyzed by pyrosequencing of the 16S rRNA. Microbiologyopen 2, 541–552 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

32.
Ortega-Retuerta, E., Joux, F., Jeffrey, W. H. & Ghiglione, J. F. Spatial variability of particle-attached and free-living bacterial diversity in surface waters from the Mackenzie River to the Beaufort Sea (Canadian Arctic). Biogeosciences 10, 2747–2759 (2013).
ADS  Google Scholar 

33.
Hollibaughl, J. T., Wongl, P. S. & Michael, C. Similarity of particle-associated and free-living bacterial communities in northern San Francisco. Water 21, 103–114 (2000).
Google Scholar 

34.
Moeseneder, M. M., Winter, C. & Herndl, G. J. Horizontal and vertical complexity of attached and free-living bacteria of the eastern Mediterranean Sea, determined by 16S rDNA and 16S rRNA fingerprints. Limnol. Oceanogr. 46, 95–107 (2001).
ADS  CAS  Google Scholar 

35.
Eloe, E. A. et al. Compositional differences in particle-associated and free-living microbial assemblages from an extreme deep-ocean environment. Environ. Microbiol. Rep. 3, 449–458 (2011).
PubMed  Google Scholar 

36.
Zhang, R., Liu, B., Lau, S., Ki, J.-S. & Qian, P.-Y. Particle-attached and free-living bacterial communities in a contrasting marine environment: Victoria Harbor, Hong Kong. FEMS Microbiol. Ecol. 61, 496–508 (2007).
CAS  PubMed  Google Scholar 

37.
Mestre, M. et al. Spatial variability of marine bacterial and archaeal communities along the particulate matter continuum. Mol. Ecol. 26, 6827–6840 (2017).
CAS  PubMed  Google Scholar 

38.
Ivars-Martinez, E. et al. Comparative genomics of two ecotypes of the marine planktonic copiotroph Alteromonas macleodii suggests alternative lifestyles associated with different kinds of particulate organic matter. ISME J. 2, 1194–1212 (2008).
CAS  PubMed  Google Scholar 

39.
Fernández-Gómez, B. et al. Ecology of marine Bacteroidetes: A comparative genomics approach. ISME J. 7, 1026–1037 (2013).
PubMed  PubMed Central  Google Scholar 

40.
Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A guide to the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. Rev. 75, 14–49 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

41.
Kasalický, V., Jezbera, J., Hahn, M. W. & Šimek, K. The diversity of the Limnohabitans genus, an important group of freshwater bacterioplankton, by characterization of 35 isolated strains. PLoS ONE 8, e58209 (2013).
ADS  PubMed  PubMed Central  Google Scholar 

42.
Simek, K. et al. Broad habitat range of the phylogenetically narrow R-BT065 cluster, representing a core group of the Betaproteobacterial genus Limnohabitans. Appl. Environ. Microbiol. 76, 631–639 (2010).
CAS  PubMed  Google Scholar 

43.
Jezberová, J., Šimek, K., Hahn, M. W., Jezbera, J. & Kasalický, V. Patterns of Limnohabitans microdiversity across a large set of freshwater habitats as revealed by reverse line blot hybridization. PLoS ONE 8, e58527 (2013).
ADS  PubMed  PubMed Central  Google Scholar 

44.
Shade, A. et al. Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity. MBio 5, 1–9 (2014).
Google Scholar 

45.
Castelle, C. J. et al. Genomic expansion of domain archaea highlights roles for organisms from new phyla in anaerobic carbon cycling. Curr. Biol. 25, 690–701 (2015).
CAS  PubMed  Google Scholar 

46.
Stahl, D. A. & de la Torre, J. R. Physiology and diversity of ammonia-oxidizing archaea. Annu. Rev. Microbiol. 66, 83–101 (2012).
CAS  PubMed  Google Scholar 

47.
Liu, X. et al. Insights into the ecology, evolution, and metabolism of the widespread Woesearchaeotal lineages. Microbiome 6, 1–16 (2018).
Google Scholar 

48.
Ortiz-Alvarez, R. & Casamayor, E. O. High occurrence of Pacearchaeota and Woesearchaeota (Archaea superphylum DPANN) in the surface waters of oligotrophic high-altitude lakes. Environ. Microbiol. Rep. 8, 210–217 (2016).
CAS  PubMed  Google Scholar 

49.
Fillol, M., Auguet, J.-C., Casamayor, E. O. & Borrego, C. M. Insights in the ecology and evolutionary history of the Miscellaneous Crenarchaeotic Group lineage. ISME J. 10, 653–677 (2016).
Google Scholar 

50.
Durbin, A. M. & Teske, A. Archaea in organic-lean and organic-rich marine subsurface sediments: an environmental gradient reflected in distinct phylogenetic lineages. Front. Microbiol. 3, 168 (2012).
PubMed  PubMed Central  Google Scholar 

51.
Fillol, M., Sànchez-Melsió, A., Gich, F. & Borrego, M. C. Diversity of Miscellaneous Crenarchaeotic Group archaea in freshwater karstic lakes and their segregation between planktonic and sediment habitats. FEMS Microbiol. Ecol. 91, fiv20 (2015).
Google Scholar 

52.
Compte-Port, S. et al. Abundance and Co-Distribution of Widespread Marine Archaeal Lineages in Surface Sediments of Freshwater Water Bodies across the Iberian Peninsula. Microb. Ecol. 74, 776–787 (2017).
PubMed  Google Scholar 

53.
Allgaier, M. & Grossart, H. Diversity and seasonal dynamics of actinobacteria populations in four lakes in Northeastern Germany. Appl. Environ. Microbiol. 72, 3489–3497 (2006).
CAS  PubMed  PubMed Central  Google Scholar 

54.
Grasshoff, K., Kremling, K. & Ehrhardt, M. Methods of Seawater Analysis (Wiley-VCH Verlag Gmbh, Weinheim, 1999).
Google Scholar 

55.
Dowd, S. E. et al. Evaluation of the bacterial diversity in the feces of cattle using 16S rDNA bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP). BMC Microbiol. 8, 125 (2008).
PubMed  PubMed Central  Google Scholar 

56.
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

57.
Desantis, T. Z. et al. Gene database and workbench compatible with ARB. (California Institute of Technology, accessed 2 October 2 2014);http://aem.asm.org/.

58.
Desantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).
CAS  PubMed  PubMed Central  Google Scholar 

59.
Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010).
CAS  PubMed  Google Scholar 

60.
Quast, C. et al. The SILVA ribosomal RNA gene database project : improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).
Google Scholar 

61.
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996 (2018).
CAS  PubMed  Google Scholar 

62.
Lozupone, C. & Knight, R. UniFrac : A new phylogenetic method for comparing microbial communitiess. Appl. Environ. Microbiol. 71, 8228–8235 (2005).
CAS  PubMed  PubMed Central  Google Scholar 

63.
Andersen, S. K., Kirkegaard, R. H., Karst, S. M. & Albertsen, M. ampvis2: An R package to analyse and visualise 16S rRNA amplicon data. BioRxiv https://doi.org/10.1101/299537 (2018).
Article  Google Scholar 

64.
R Development Core Team. R: A language and environment for statistical computing. ISBN: 3-900051-07-0 (2011).

65.
Dufrene, M. & Legendre, P. Species assemblages and indicator species: The need for flexible asymmetrical approach. Ecol. Monogr. 67, 345–366 (1997).
Google Scholar 

66.
De Cáceres, M. & Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 90, 3566–3574 (2009).
PubMed  Google Scholar 

67.
Aßhauer, K. P., Wemheuer, B., Daniel, R. & Meinicke, P. Tax4Fun: Predicting functional profiles from metagenomic 16S rRNA data. Bioinformatics 31, 2882–2884 (2015).
PubMed  PubMed Central  Google Scholar 

68.
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).
MathSciNet  MATH  Google Scholar 

69.
Wickham, H. ggplot2. Elegant Grsaphics for Data Analysis (Springer, New York , 2009). https://doi.org/10.1007/978-0-387-98141-3.
Google Scholar  More

Overview of air pollutants in Shanghai during 2015–2018
The average mass concentrations of the target pollutants during 2015–2018 were analyzed. We used the cumulative distribution of daily average values of PM2.5, PM10, NO2, SO2, CO, and O3_8h to determine the number of days during which Shanghai municipality was exposed to air pollution (Fig. 1)24. For at least some half-days in 2015 (2016, 2017, 2018), Shanghai municipality was exposed to average values higher than 59 (50, 45, 40) μg m−3 for PM2.5, 52 (48, 47, 40) μg m−3 for PM10, 45 (43, 47, 44) μg m−3 for O3_8h, 48 (45, 47, 44) μg m−3 for NO2, 13 (12, 9, 8) μg m−3 for SO2, and 18 (18, 18, 15) mg m−3 for CO. This indicates a decrease in the number of days per year in which Shanghai residents were exposed to high concentrations of PM2.5, PM10, NO2, SO2, and CO.
Figure 1

(a–f) Cumulative distribution of daily average mean concentrations of air pollutants in Shanghai municipality.

Full size image

Temporal variations in air pollutants
Following implementation of the six-round, 3-year environmental protection action plan, ambient air quality in Shanghai municipality has improved slightly. In 2018, the average annual concentration of SO2 and PM10 in Shanghai municipality was 10 μg m−3 and 51 μg m−3 respectively, the 90th percentile of O3_8h concentration was 160 μg m−3, and daily CO concentration was within the range 0.4–2.0 mg m−3. All these concentrations met the national Level I or Level II for annual mean ambient air quality. However, the average annual concentration of NO2 and PM2.5 in the city in 2018 was 42 μg m−3 and 36 μg m−3, respectively, which did not meet the Level II annual mean level air quality standard. Moreover, monitoring data for the past 4 years show that the annual mean concentrations of NO2 and PM2.5 in Shanghai are generally declining, but they still exceed the national Level II air quality standards. The daily maximum 8-h average, 24-h average, and annual mean concentrations of six air pollutants in Shanghai municipality during 2015–2018 are summarized in Fig. 2. Compared with 2015, the average concentration in 2018 decreased by 32.08%, 26.09%, 0.62%, 41.18%, 8.70%, and 22.09% for PM2.5, PM10, O3_8h, SO2, NO2, and CO, respectively. The large decrease in SO2 in the air Shanghai municipality was consistent with the overall trend in annual mean concentration of SO2 in China8. This indicates effective control of combustion emissions and implementation of desulfurization systems8,31. Our results also indicated that more than 70% of the total mass of PM10 was composed of PM2.5, which is close to the ratio reported in previous studies8,24. The decreases in CO and NO2 concentrations were mainly attributable to effective regulation of coal combustion emissions and traffic-related emissions8,31,32,33. The reductions amplitudes were lower for CO and NO2 compared with PM2.5, PM10, and SO2, which may be related to the rapid increase in vehicles in Chinese cities8. No clear decrease was observed for the 90th percentile of O3_8h concentration in this study. Air pollution has gradually changed from the conventional coal combustion type to mixed coal combustion/motor vehicle emission type3, reflecting the rapid increase in the number of motor vehicles in Shanghai municipality34. This poses enormous challenges for air pollution control and environmental management.
Figure 2

Temporal variations in 24-h average concentrations and annual mean concentrations of air pollutants in Shanghai municipality, 2015–2018.

Full size image

Major pollutants and non-attainment days
The number of days meeting the mean concentration limits of ‘Chinese ambient air quality standards’ (CAAQS) in Shanghai municipality during 2015–2018 was examined (Fig. 3). In 2015 (2016, 2017, 2018), 18.6% (27.5%, 33.6%, 41.5%), 77.9% (85.3%, 92.6%, 91.5%), 35.8% (40.1%, 35.2%, 41.0%), 99.5% (100%, 100%,100%), 99.7 (100%, 100%, 100%), and 58.4% (67.2%, 57.0%, 60.8%) of days met the concentration limit in CAAQS Grade II for 24-h average PM2.5, PM10, NO2, SO2, CO, and maximum 8-h average O3. Compared with 2015, the number of days in 2018 that met the level in CAAQS Grade II increased by 124.3%, 17.5%, 4.1%, 14.5%, 0.5%, and 0.3% for PM2.5, PM10, O3_8h, SO2, NO2, and CO, respectively. The number of days with excellent air quality increased from 55 in 2015 to 93 in 2018, while the number of days with ‘good’ air quality remained consistent at 203 days between 2015 and 2018.
Figure 3

Number of days per year on which each pollutant was designated a “major pollutant” (different shapes) and air quality level (different colors) in Shanghai municipality.

Full size image

The most frequent “major pollutant” in Shanghai municipality was O3, followed by PM2.5 and then NO2 and PM10. In comparison, SO2 and CO were the “major pollutant” considerably less frequently. The number of days on which PM2.5, O3, NO2, and PM10 was designated the “major pollutant” was 120 (104, 67, 61), 110 (84, 126, 113), 50 (67, 79, 63) and 16 (13, 13, 14) in 2015 (2016, 2017, 2018), respectively. The low incidence of SO2 as a “major pollutant” again indicated effective control of coal combustion and implementation of desulphurization systems8,31. Compared with 2015, the incidence of O3 as a major pollutant in Shanghai increased to reach its highest value in 2017. This is consistent with the 90th percentile of O3_8h concentration, which also peaked in 2017. Previous studies have suggested that O3 is a complex secondary pollutant related to solar radiation, NOx, volatile organic compounds (VOC), and vertical transport in the boundary layer8, factors that are difficult to control effectively35,36. While the number of polluted days with PM2.5 concentrations over 75 μg m−3 decreased from 2015 to 2018, the complex mixture of PM2.5 and O3 in the air is still a challenge to continuous improvement of air quality in Shanghai municipality8,24.
There were seasonal variations in the concentrations of each pollutant (Fig. 4a), and thus the days on which the air quality standard was exceeded (non-attainment days) were not equally distributed throughout the year (Fig. 4b), which is consistent with findings in previous studies24,37. November, December, January, February, and March were the dominant months with non-attainment days for PM2.5 in Shanghai municipality, while April, May, June, July, August, and September were the dominant months with non-attainment days for O3_8h. Overall, winter months had the largest number of polluted days and highest mean concentration of PM2.5, followed by spring, autumn, and summer, which is consistent with previous findings16. This trend has been mainly attributed to coal-fired heating of buildings16,38,39,40. Summertime O3 pollution in Shanghai was much more severe than in the other seasons (Fig. 4b), and the probability of O3_8h exceeding the CAAQS Grade II value was highest in July (11.25 ± 5.85 day), followed by August (6.25 ± 4.65 day), May (5.75 ± 3.2 day), and June (5.5 ± 1.29 day). This is consistent with findings in previous studies that summer is the O3 episode season in Chinese megacity clusters41,42. Polluted days with NO2  > 80 μg m−3 were mainly observed during winter and spring. The low probability of SO2 exceeding the CAAQS Grade II value reflected the stringent SO2 emission regulations in Shanghai municipality31.
Figure 4

(a) Average concentration of the pollutants PM2.5, PM10, SO2, and NO2 and (b) percentage of non-attainment days and major pollutant on polluted days in each month during 2015–2018.

Full size image

Correlations between air pollutants
Different air pollutants were significantly correlated (p  More

Assessment of data quality
National Allergy Bureau pollen concentration data quality
To assess the quality of NAB data overall, we analyzed gaps in data recording and percentages of missing data in daily NAB measurements from each station from January to December of each year. Availability of pollen concentration data varied widely by station, with percent of days per year missing pollen data ranging from 0% (e.g. San Antonio, TX; 2012) up to 100% (e.g. Oklahoma City, OK; 2014) (Supplementary Fig. 5A). Common days missing data were at the beginning of the year, the end of the year, and on weekends (data not shown). Although NAB directs its certified pollen counting stations to collect data for a minimum of 3 days per week, gaps in pollen collection within 10 days before and after the first recorded high pollen concentration (200 grains/m3) spanned up to 10 consecutive days (Supplementary Fig. 5B). Over the span of the year, the median gap between measurements across station-years was 5 days (IQR = 3.12). The date of first available pollen concentration data ranged from day 1 of the year to day 96 with a median day of 3 (IQR = 1.27) (Supplementary Fig. 5C). For the majority of station-years (64.5%), the first day of the first recorded data for the year was the same as the first day with a non-zero pollen count.
Google trends search data quality
We analyzed GT daily data quality per DMA region during the early pollen season, from January to June of each year. The percent of missing days of GT data ranged from 0–93% (lowest missing from San Jose CA 2013 and highest missing from Midland TX 2012, respectively) with median and IQR = 33% (8–51%) (Supplementary Fig. 6A). Earlier years of GT data had more daily search volumes not quantified (referred to here as “missing”) due to lower search volumes and not meeting Google’s threshold for inclusion (Supplementary Fig. 6B). Variation was observed between GT download iterations, as GT provides a random sample of its data for each download (Supplementary Fig. 2A,B).
Factors associated with data quality
Biogeography and population characteristics were assessed for their impact on data quality, specifically overall ecoregion classification, total annual precipitation and mean spring temperature (chosen for their likely impact pollen production and seasonality34), as well as TV-homes, a combinatorial metric for population size and media use.
With respect to ecoregion, the majority of NAB stations were classified as Eastern Temperature Forests (67.6%) or Great Plains (21.6%). Other ecoregions each represented 5% or less of NAB stations: Marine West Coast Forest, Mediterranean California, and Northwestern Forested Mountains. U.S. ecoregions not represented by NAB stations included: Northern Forests (as in Vermont), Tropical Wet Forests (as in southern Florida), North American Deserts (as in Nevada), Southern Semi-Arid Highlands (as in southeastern Arizona), and Temperate Sierras (as in southwestern New Mexico). As a whole, NAB stations in Great Plains ecoregions had slightly higher data quality (p  More

1.
Wang Z, Walker GW, Muir DCG, Nagatani-Yoshida K. Toward a global understanding of chemical pollution: a first comprehensive analysis of national and regional chemical inventorie. Environ Sci Technol. 2020;54:2575–84.
CAS  Article  Google Scholar 
2.
Geyer R, Jambeck JR, Law KL. Production, use, and fate of all plastics ever made. Sci Adv. 2017;3:e1700782.
Article  Google Scholar 

3.
González-Gaya B, Fernández-Pinos MC, Morales L, Méjanelle L, Abad E, Piña B, et al. High atmosphere–ocean exchange of semivolatile aromatic hydrocarbons. Nat Geosci. 2016;9:438–42.
Article  Google Scholar 

4.
Cerro-Gálvez E, Casal P, Lundin D, Piña B, Pinhassi J, Dachs J, et al. Microbial responses to anthropogenic dissolved organic carbon in the Arctic and Antarctic coastal seawaters. Environ Microbiol. 2019;21:1466–81.
Article  Google Scholar 

5.
Carlson CA, Hansell DA. DOM sources, sinks, reactivity, and budgets. In: Carlson CA, Hansell DA. editors. Biogeochemistry of marine dissolved organic matter. San Diego (USA): Academic press; 2015. p. 65–126

6.
Romera-Castillo C, Pinto M, Langer TM, Álvarez-Salgado XA, Herndl GJ. Dissolved organic carbon leaching from plastics stimulates microbial activity in the ocean. Nat Commun. 2018;9:1430.
Article  Google Scholar 

7.
Cerro-Gálvez E, Sala MM, Marrasé C, Gasol JM, Dachs J, Vila-Costa M. Modulation of microbial growth and enzymatic activities in the marine environment due to exposure to organic contaminants of emerging concern and hydrocarbons. Sci Total Environ. 2019;678:486–98.
Article  Google Scholar 

8.
González-Gaya B, Martínez-Varela A, Vila-Costa M, Casal P, Cerro-Gálvez E, Berrojalbiz N, et al. Biodegradation as an important sink of aromatic hydrocarbons in the oceans. Nat Geosci. 2019;12:119–25.
Article  Google Scholar 

9.
Vila-Costa M, Sebastián M, Pizarro M, Cerro-Gálvez E, Lundin D, Gasol JM, et al. Microbial consumption of organophosphate esters in seawater under phosphorus limited conditions. Sci Rep. 2019;9:1–11.
CAS  Article  Google Scholar 

10.
Cerro-Galvez E, Roscales JL, Jiménez B, Sala MM, Dachs J, Vila-Costa M. Microbial responses to perfluoroalkyl substances and perfluorooctanesulfonate (PFOS) desulfurization in the Antarctic marine environment. Water Res. 2020;171:115434. 
CAS  Article  Google Scholar 

11.
Fernández-Pinos MC, Vila-Costa M, Arrieta JM, Morales L, González-Gaya B, Piña B, et al. Dysregulation of photosynthetic genes in oceanic Prochlorococcus populations exposed to organic pollutants. Sci Rep. 2017;7:8029.
Article  Google Scholar 

12.
Tetu SG, Sarker I, Schrameyer V, Pickford R, Elbourne LD, Moore LR, et al. Plastic leachates impair growth and oxygen production in Prochlorococcus, the ocean’s most abundant photosynthetic bacteria. Commun Biol. 2019;2:1–9.
Article  Google Scholar 

13.
Buttigieg PL, Fadeev E, Bienhold C, Hehemann L, Offre P, Boetius A. Marine microbes in 4D – using time series observation to assess the dynamics of the ocean microbiome and its links to ocean health. Curr Opin Microbiol. 2018;43:169–85.
Article  Google Scholar  More

1.
Greening C, Constant P, Hards K, Morales SE, Oakeshott JG, Russell RJ, et al. Atmospheric hydrogen scavenging: from enzymes to ecosystems. Appl Environ Microbiol. 2015;81:1190–9.
PubMed  PubMed Central  Google Scholar 
2.
Ehhalt DH, Rohrer F. The tropospheric cycle of H2: a critical review. Tellus B. 2009;61:500–35.
Google Scholar 

3.
Constant P, Poissant L, Villemur R. Tropospheric H2 budget and the response of its soil uptake under the changing environment. Sci Total Environ. 2009;407:1809–23.
CAS  PubMed  Google Scholar 

4.
Greening C, Biswas A, Carere CR, Jackson CJ, Taylor MC, Stott MB, et al. Genomic and metagenomic surveys of hydrogenase distribution indicate H2 is a widely utilised energy source for microbial growth and survival. ISME J. 2016;10:761–77.
CAS  PubMed  Google Scholar 

5.
Kanno M, Constant P, Tamaki H, Kamagata Y. Detection and isolation of plant-associated bacteria scavenging atmospheric molecular hydrogen. Environ Microbiol. 2015;18:2495–506.
Google Scholar 

6.
Kessler AJ, Chen Y-J, Waite DW, Hutchinson T, Koh S, Popa ME, et al. Bacterial fermentation and respiration processes are uncoupled in permeable sediments. Nat Microbiol. 2019;4:1014–23.
CAS  PubMed  Google Scholar 

7.
Khdhiri M, Hesse L, Popa ME, Quiza L, Lalonde I, Meredith LK, et al. Soil carbon content and relative abundance of high affinity H2-oxidizing bacteria predict atmospheric H2 soil uptake activity better than soil microbial community composition. Soil Biol Biochem. 2015;85:1–9.
CAS  Google Scholar 

8.
Lynch RC, Darcy JL, Kane NC, Nemergut DR, Schmidt SK. Metagenomic evidence for metabolism of trace atmospheric gases by high-elevation desert actinobacteria. Front Microbiol. 2014;5:698.
PubMed  PubMed Central  Google Scholar 

9.
Ji M, Greening C, Vanwonterghem I, Carere CR, Bay SK, Steen JA, et al. Atmospheric trace gases support primary production in Antarctic desert surface soil. Nature. 2017;552:400–3.
CAS  PubMed  Google Scholar 

10.
Constant P, Chowdhury SP, Pratscher J, Conrad R. Streptomycetes contributing to atmospheric molecular hydrogen soil uptake are widespread and encode a putative high-affinity [NiFe]-hydrogenase. Environ Microbiol. 2010;12:821–9.
CAS  PubMed  Google Scholar 

11.
Constant P, Chowdhury SP, Hesse L, Pratscher J, Conrad R. Genome data mining and soil survey for the novel Group 5 [NiFe]-hydrogenase to explore the diversity and ecological importance of presumptive high-affinity H2-oxidizing bacteria. Appl Environ Microbiol. 2011;77:6027–35.
CAS  PubMed  PubMed Central  Google Scholar 

12.
Greening C, Berney M, Hards K, Cook GM, Conrad R. A soil actinobacterium scavenges atmospheric H2 using two membrane-associated, oxygen-dependent [NiFe] hydrogenases. Proc Natl Acad Sci USA. 2014;111:4257–61.
CAS  PubMed  Google Scholar 

13.
Schäfer C, Friedrich B, Lenz O. Novel, oxygen-insensitive group 5 [NiFe]-hydrogenase in Ralstonia eutropha. Appl Environ Microbiol. 2013;79:5137–45.
PubMed  PubMed Central  Google Scholar 

14.
Constant P, Poissant L, Villemur R. Isolation of Streptomyces sp. PCB7, the first microorganism demonstrating high-affinity uptake of tropospheric H2. ISME J. 2008;2:1066–76.
CAS  PubMed  Google Scholar 

15.
Meredith LK, Rao D, Bosak T, Klepac-Ceraj V, Tada KR, Hansel CM, et al. Consumption of atmospheric hydrogen during the life cycle of soil-dwelling actinobacteria. Environ Microbiol Rep. 2014;6:226–38.
CAS  PubMed  Google Scholar 

16.
Greening C, Carere CR, Rushton-Green R, Harold LK, Hards K, Taylor MC, et al. Persistence of the dominant soil phylum Acidobacteria by trace gas scavenging. Proc Natl Acad Sci USA. 2015;112:10497–502.
CAS  PubMed  Google Scholar 

17.
Myers MR, King GM. Isolation and characterization of Acidobacterium ailaaui sp. nov., a novel member of Acidobacteria subdivision 1, from a geothermally heated Hawaiian microbial mat. Int J Syst Evol Microbiol. 2016;66:5328–35.
CAS  PubMed  Google Scholar 

18.
Islam ZF, Cordero PRF, Feng J, Chen Y-J, Bay S, Gleadow RM, et al. Two Chloroflexi classes independently evolved the ability to persist on atmospheric hydrogen and carbon monoxide. ISME J. 2019;13:1801–13.
CAS  PubMed  PubMed Central  Google Scholar 

19.
Schmitz RA, Pol A, Mohammadi SS, Hogendoorn C, van Gelder AH, Jetten MSM, et al. The thermoacidophilic methanotroph Methylacidiphilum fumariolicum SolV oxidizes subatmospheric H2 with a high-affinity, membrane-associated [NiFe] hydrogenase. ISME J 2020;14:1223–32.
CAS  PubMed  PubMed Central  Google Scholar 

20.
Berney M, Cook GM. Unique flexibility in energy metabolism allows mycobacteria to combat starvation and hypoxia. PLoS One. 2010;5:e8614.
PubMed  PubMed Central  Google Scholar 

21.
Berney M, Greening C, Conrad R, Jacobs WR, Cook GM. An obligately aerobic soil bacterium activates fermentative hydrogen production to survive reductive stress during hypoxia. Proc Natl Acad Sci USA. 2014;111:11479–84.
CAS  PubMed  Google Scholar 

22.
Cordero PRF, Grinter R, Hards K, Cryle MJ, Warr CG, Cook GM, et al. Two uptake hydrogenases differentially interact with the aerobic respiratory chain during mycobacterial growth and persistence. J Biol Chem. 2019;294:18980–91.
CAS  PubMed  Google Scholar 

23.
Greening C, Villas-Bôas SG, Robson JR, Berney M, Cook GM. The growth and survival of Mycobacterium smegmatis is enhanced by co-metabolism of atmospheric H2. PLoS One. 2014;9:e103034.
PubMed  PubMed Central  Google Scholar 

24.
Liot Q, Constant P. Breathing air to save energy – new insights into the ecophysiological role of high-affinity [NiFe]-hydrogenase in Streptomyces avermitilis. Microbiologyopen. 2016;5:47–59.
CAS  PubMed  Google Scholar 

25.
Søndergaard D, Pedersen CNS, Greening C. HydDB: a web tool for hydrogenase classification and analysis. Sci Rep. 2016;6:34212.
PubMed  PubMed Central  Google Scholar 

26.
Papen H, Kentemich T, Schmülling T, Bothe H. Hydrogenase activities in cyanobacteria. Biochimie. 1986;68:121–32.
CAS  PubMed  Google Scholar 

27.
Houchins JP, Burris RH. Occurrence and localization of two distinct hydrogenases in the heterocystous cyanobacterium Anabaena sp. Strain 7120. J Bacteriol. 1981;146:209–14.
CAS  PubMed  PubMed Central  Google Scholar 

28.
Tamagnini P, Axelsson R, Lindberg P, Oxelfelt F, Wünschiers R, Lindblad P. Hydrogenases and hydrogen metabolism of cyanobacteria. Microbiol Mol Biol Rev. 2002;66:1–20.
CAS  PubMed  PubMed Central  Google Scholar 

29.
Bothe H, Schmitz O, Yates MG, Newton WE. Nitrogen fixation and hydrogen metabolism in cyanobacteria. Microbiol Mol Biol Rev. 2010;74:529–51.
CAS  PubMed  PubMed Central  Google Scholar 

30.
Koch H, Galushko A, Albertsen M, Schintlmeister A, Gruber-Dorninger C, Lucker S, et al. Growth of nitrite-oxidizing bacteria by aerobic hydrogen oxidation. Science. 2014;345:1052–4.
CAS  PubMed  Google Scholar 

31.
Drobner E, Huber H, Stetter KO. Thiobacillus ferrooxidans, a facultative hydrogen oxidizer. Appl Environ Microbiol. 1990;56:2922–3.
CAS  PubMed  PubMed Central  Google Scholar 

32.
Berney M, Greening C, Hards K, Collins D, Cook GM. Three different [NiFe] hydrogenases confer metabolic flexibility in the obligate aerobe Mycobacterium smegmatis. Environ Microbiol. 2014;16:318–30.
CAS  PubMed  Google Scholar 

33.
Islam ZF, Cordero PRF, Greening C. Putative iron-sulfur proteins are required for hydrogen consumption and enhance survival of mycobacteria. Front Microbiol. 2019;10:2749.
PubMed  PubMed Central  Google Scholar 

34.
Taylor SW, Fahy E, Zhang B, Glenn GM, Warnock DE, Wiley S, et al. Characterization of the human heart mitochondrial proteome. Nat Biotechnol. 2003;21:281–6.
CAS  PubMed  Google Scholar 

35.
Park D, Kim H, Yoon S. Nitrous oxide reduction by an obligate aerobic bacterium, Gemmatimonas aurantiaca strain T-27. Appl Environ Microbiol. 2017;83:e00502–17.
PubMed  PubMed Central  Google Scholar 

36.
Razzell WE, Trussell PC. Isolation and properties of an iron-oxidizing Thiobacillus. J Bacteriol. 1963;85:595–603.
CAS  PubMed  PubMed Central  Google Scholar 

37.
Valdés J, Pedroso I, Quatrini R, Dodson RJ, Tettelin H, Blake R, et al. Acidithiobacillus ferrooxidans metabolism: from genome sequence to industrial applications. BMC Genomics. 2008;9:597.
PubMed  PubMed Central  Google Scholar 

38.
Hanada S, Hiraishi A, Shimada K, Matsuura K. Chloroflexus aggregans sp. nov., a filamentous phototrophic bacterium which forms dense cell aggregates by active gliding movement. Int J Syst Evol Microbiol. 1995;45:676–81.
CAS  Google Scholar 

39.
Otaki H, Everroad RC, Matsuura K, Haruta S. Production and consumption of hydrogen in hot spring microbial mats dominated by a filamentous anoxygenic photosynthetic bacterium. Microbes Environ. 2009;27:293–9.
Google Scholar 

40.
Kawai S, Nishihara A, Matsuura K, Haruta S. Hydrogen-dependent autotrophic growth in phototrophic and chemolithotrophic cultures of thermophilic bacteria, Chloroflexus aggregans and Chloroflexus aurantiacus, isolated from Nakabusa hot springs. FEMS Microbiol Lett. 2019;366:fnz122.
CAS  PubMed  Google Scholar 

41.
Heinhorst S, Baker SH, Johnson DR, Davies PS, Cannon GC, Shively JM. Two copies of form I RuBisCO genes in Acidithiobacillus ferrooxidans ATCC 23270. Curr Microbiol. 2002;45:115–17.
CAS  PubMed  Google Scholar 

42.
Klatt CG, Bryant DA, Ward DM. Comparative genomics provides evidence for the 3-hydroxypropionate autotrophic pathway in filamentous anoxygenic phototrophic bacteria and in hot spring microbial mats. Environ Microbiol. 2007;9:2067–78.
CAS  PubMed  Google Scholar 

43.
Zhang H, Sekiguchi Y, Hanada S, Hugenholtz P, Kim H, Kamagata Y, et al. Gemmatimonas aurantiaca gen. nov., sp. nov., a Gram-negative, aerobic, polyphosphate-accumulating micro-organism, the first cultured representative of the new bacterial phylum Gemmatimonadetes phyl. nov. Int J Syst Evol Microbiol. 2003;53:1155–63.
CAS  PubMed  Google Scholar 

44.
Zammit CM, Mutch LA, Watling HR, Watkin ELJ. The recovery of nucleic acid from biomining and acid mine drainage microorganisms. Hydrometallurgy. 2011;108:87–92.
CAS  Google Scholar 

45.
Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, et al. Primer3—new capabilities and interfaces. Nucleic Acids Res. 2012;40:e115.
CAS  PubMed  PubMed Central  Google Scholar 

46.
Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9.
CAS  PubMed  PubMed Central  Google Scholar 

47.
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.
CAS  PubMed  PubMed Central  Google Scholar 

48.
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;6:1925–7.
Google Scholar 

49.
Kawasumi T, Igarashi Y, Kodama T, Minoda Y. Hydrogenobacter thermophilus gen. nov., sp. nov., an extremely thermophilic, aerobic, hydrogen-oxidizing bacterium. Int J Syst Evol Microbiol. 1984;34:5–10.
CAS  Google Scholar 

50.
Klenk H-P, Lapidus A, Chertkov O, Copeland A, Del Rio TG, Nolan M, et al. Complete genome sequence of the thermophilic, hydrogen-oxidizing Bacillus tusciae type strain (T2 T) and reclassification in the new genus, Kyrpidia gen. nov. as Kyrpidia tusciae comb. nov. and emendation of the family Alicyclobacilla. Stand Genom Sci. 2011;5:121.
CAS  Google Scholar 

51.
Hogendoorn C, Pol A, Picone N, Cremers G, van Alen TA, Gagliano AL, et al. Hydrogen and carbon monoxide-utilizing Kyrpidia spormannii species from Pantelleria Island, Italy. Front Microbiol. 2020;11:951.
PubMed  PubMed Central  Google Scholar 

52.
Hedrich S, Johnson DB. Aerobic and anaerobic oxidation of hydrogen by acidophilic bacteria. FEMS Microbiol Lett. 2013;349:40–45.
CAS  PubMed  Google Scholar 

53.
Grostern A, Alvarez-Cohen L. RubisCO-based CO2 fixation and C1 metabolism in the actinobacterium Pseudonocardia dioxanivorans CB1190. Environ Microbiol. 2013;15:3040–53.
CAS  PubMed  Google Scholar 

54.
Auernik KS, Kelly RM. Physiological versatility of the extremely thermoacidophilic archaeon Metallosphaera sedula supported by transcriptomic analysis of heterotrophic, autotrophic, and mixotrophic growth. Appl Environ Microbiol. 2010;76:931–5.
CAS  PubMed  Google Scholar 

55.
Mohammadi S, Pol A, van Alen TA, Jetten MSM, Op den Camp HJM. Methylacidiphilum fumariolicum SolV, a thermoacidophilic ‘Knallgas’ methanotroph with both an oxygen-sensitive and -insensitive hydrogenase. ISME J. 2017;11:945–58.
CAS  PubMed  Google Scholar 

56.
Tveit AT, Hestnes AG, Robinson SL, Schintlmeister A, Dedysh SN, Jehmlich N, et al. Widespread soil bacterium that oxidizes atmospheric methane. Proc Natl Acad Sci USA. 2019;116:8515–24.
CAS  PubMed  Google Scholar 

57.
Conrad R. Soil microorganisms oxidizing atmospheric trace gases (CH4, CO, H2, NO). Indian J Microbiol. 1999;39:193–203.
Google Scholar 

58.
Spear JR, Walker JJ, McCollom TM, Pace NR. Hydrogen and bioenergetics in the Yellowstone geothermal ecosystem. Proc Natl Acad Sci USA. 2005;102:2555–60.
CAS  PubMed  Google Scholar 

59.
Ferrera I, Sanchez O. Insights into microbial diversity in wastewater treatment systems: How far have we come? Biotechnol Adv. 2016;34:790–802.
CAS  PubMed  Google Scholar 

60.
Mielke RE, Pace DL, Porter T, Southam G. A critical stage in the formation of acid mine drainage: Colonization of pyrite by Acidithiobacillus ferrooxidans under pH‐neutral conditions. Geobiology. 2003;1:81–90.
CAS  Google Scholar 

61.
Constant P, Chowdhury SP, Hesse L, Conrad R. Co-localization of atmospheric H2 oxidation activity and high affinity H2-oxidizing bacteria in non-axenic soil and sterile soil amended with Streptomyces sp. PCB7. Soil Biol Biochem. 2011;43:1888–93.
CAS  Google Scholar 

62.
Cordero PRF, Bayly K, Leung PM, Huang C, Islam ZF, Schittenhelm RB, et al. Atmospheric carbon monoxide oxidation is a widespread mechanism supporting microbial survival. ISME J. 2019;13:2868–81.
CAS  PubMed  PubMed Central  Google Scholar 

63.
Eichner MJ, Basu S, Gledhill M, de Beer D, Shaked Y. Hydrogen dynamics in Trichodesmium colonies and their potential role in mineral iron acquisition. Front Microbiol. 2019;10:1565.
PubMed  PubMed Central  Google Scholar 

64.
Houchins JP, Burris RH. Comparative characterization of two distinct hydrogenases from Anabaena sp. strain 7120. J Bacteriol. 1981;146:215–21.
CAS  PubMed  PubMed Central  Google Scholar 

65.
Greening C, Grinter R, Chiri E. Uncovering the metabolic strategies of the dormant microbial majority: towards integrative approaches. mSystems. 2019;4:e00107–19.
CAS  PubMed  PubMed Central  Google Scholar  More