| Literature DB >> 26733966 |
Chandan Mukherjee1, Rajojit Chowdhury1, Krishna Ray1.
Abstract
Phosphorus (P), an essential element required for crop growth has no substitute. The global food security depends on phosphorus availability in soil for crop production. World phosphorus reserves are fast depleting and with an annual increase of 2.3% in phosphorus demand, the current reserves will be exhausted in coming 50-100 years. India and other Western countries are forced to import phosphorus fertilizers at high costs to meet their agricultural demands due to uneven distribution of phosphate rocks on earth. The present study from India, aims to draw attention to an unnoticed source of phosphorus being wasted as parboiled rice mill effluent and subsequent bio-recovery of the valuable element from this unconventional source. The research was conducted in West Bengal, India, a state with the highest number of parboiled rice mills where its effluent carries on an average ~40 mg/L of soluble phosphorus. Technology to recover and recycle this wastewater P in India in a simple, inexpensive mode is yet to be optimized. Our strategy to use microalgae, Chlorella sp. and cyanobacteria, Cyanobacterium sp., Lyngbya sp., and Anabaena sp. to sequester the excess phosphorus from the effluent as polyphosphate inclusions and its subsequent recycling as slow and moderate release phosphorus biofertilizers to aid plant growth, preventing phosphorus loss and pollution, is a contemporary venture to meet the need of the hour. These polyphosphate accumulating microorganisms play a dual role of remediation and recovery of phosphorus, preliminarily validated in laboratory scale.Entities:
Keywords: global food security; microalgae and cyanobacteria; parboiled rice mill effluent; phosphorus biofertilizers; phosphorus pollution; phosphorus scarcity; polyphosphates
Year: 2015 PMID: 26733966 PMCID: PMC4686675 DOI: 10.3389/fmicb.2015.01421
Source DB: PubMed Journal: Front Microbiol ISSN: 1664-302X Impact factor: 5.640
Figure 1Soluble phosphorus in parboiled rice mill effluent and its remediation by microalgae and cyanobacteria as polyphosphate accumulators. (A) Box plot showing the phosphorus concentration in the rice mill effluent samples collected from West Bengal, India. “n” is the number of rice mills from where the samples were collected. Solid line in the box represents median values. Box represents 25–75% percentiles; range bar represents 5 and 95% percentiles, and dots beyond these bars represent values outside the 95% confidence interval. (B) Percentage removal of phosphorus from a rice mill effluent sample (initial phosphorus concentration was 35 mg/L) by different microalgae and cyanobacteria over a period of 21 days. All the bars on the graph represent the average data of 10 replicate experiments. Error bars were calculated on the basis of standard deviation of the data using the software Microsoft Excel. (C) Cell-free extract of polyphosphate granules stained with the Toluidine Blue dye as observed under bright field microscope. (D–G) DAPI staining of polyphosphate granules present in Chlorella sp. isolate 10.2 (Accession No. KJ654316), Cyanobacterium sp. isolate Fardillapur (Accession No. JX023443), Lyngbya sp. isolate 2.1 (Accession No. KF644563) and Anabaena sp. isolate A2C2 (Accession No. KF644564) observed under confocal microscope. The yellowish-green fluorescence indicates the presence of polyphosphate granules in the cells whereas the cells devoid of the granules emit blue fluorescence.
Figure 2Recycled polyphosphates as substitute to phosphorus fertilizers. (A) Table showing the rate of conversion of polyphosphates accumulated by the microalgae and cyanobacteria into soluble phosphorus and comparison of its release to conventional chemical phosphorus fertilizers commercialized widely in India. (B) Polyphosphate releases soluble phosphorus at a comparable (maximum at 45 days) but slower rate (as reflected in initial 10 days) with recommended dose of superphosphate and NPK at 8–10 cm depth of soil. All the points on the graph represent the average data of 10 replicate experiments. Error bars were calculated on the basis of standard deviation of the data using the software SigmaPlot 13.0.