Dataset Information

Spatiotemporal variation of nitrate concentrations in soil and groundwater of an intensely polluted agricultural area.

ABSTRACT: Nitrate pollution in groundwater is a serious problem in many parts of the world. However, due to the diffuse and common spatially over-lapping character of potential several non-point pollution sources, it is often difficult to distinguish main nitrate sources responsible for the pollution. For this purpose, we present a novel methodology applied to groundwater for an intensely polluted area. Groundwater samples were collected monthly from April 2017 to March 2018 in Shimabara City, Nagasaki, Japan. Soil samples were collected seasonally at soil surface and 50 cm depth at 10 locations during the same period. Sequential extraction by water and extract agents was performed using calcium phosphate for anions and strontium chloride for cations. Mean nitrate concentration in groundwater close to a livestock waste disposal site (hereinafter called "LWDS") was 14.2 mg L^-1, which is exceeding Japanese drinking water standards (10 mg L^-1). We used coprostanol concentration, which is a fecal pollution indicator, to identify pollution sources related to livestock waste. For this purpose, we measured coprostanol (5?) and cholestanol (5?) and then calculated the sterol ratio (5?/(5??+?5?)). The ratios for three groundwater sampling sites were 0.28, 0.26, and 0.10, respectively. The sterol ratios indicated no pollution (

SUBMITTER: Nakagawa K

PROVIDER: S-EPMC7844300 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Spatiotemporal variation of nitrate concentrations in soil and groundwater of an intensely polluted agricultural area.

Nakagawa Kei K Amano Hiroki H Persson Magnus M Berndtsson Ronny R

Scientific reports 20210128 1

Nitrate pollution in groundwater is a serious problem in many parts of the world. However, due to the diffuse and common spatially over-lapping character of potential several non-point pollution sources, it is often difficult to distinguish main nitrate sources responsible for the pollution. For this purpose, we present a novel methodology applied to groundwater for an intensely polluted area. Groundwater samples were collected monthly from April 2017 to March 2018 in Shimabara City, Nagasaki, J ...[more]

PMID: 33510403

Similar Datasets

Project description:BackgroundClimate change produces extremes in both temperature and precipitation causing increased drought severity and increased reliance on groundwater resources. Agricultural practices, which rely on groundwater, are sensitive to but also sources of contaminants, including nitrate. How agricultural contamination drives groundwater geochemistry through microbial metabolism is poorly understood.MethodsOn an active cow dairy in the Central Valley of California, we sampled groundwater from three wells at depths of 4.3 m (two wells) and 100 m (one well) below ground surface (bgs) as well as an effluent surface water lagoon that fertilizes surrounding corn fields. We analyzed the samples for concentrations of solutes, heavy metals, and USDA pathogenic bacteria of the Escherichia coli and Enterococcus groups as part of a long term groundwater monitoring study. Whole metagenome shotgun sequencing and assembly revealed taxonomic composition and metabolic potential of the community.ResultsElevated nitrate and dissolved organic carbon occurred at 4.3m but not at 100m bgs. Metagenomics confirmed chemical observations and revealed several Planctomycete genomes, including a new Brocadiaceae lineage and a likely Planctomycetes OM190, as well novel diversity and high abundance of nano-prokaryotes from the Candidate Phyla Radiation (CPR), the Diapherotrites, Parvarchaeota, Aenigmarchaeota, Nanoarchaeota, Nanohaloarchaea (DPANN) and the Thaumarchaeota, Aigarchaeota, Crenarchaeota, Korarchaeota (TACK) superphyla. Pathway analysis suggests community interactions based on complimentary primary metabolic pathways and abundant secondary metabolite operons encoding antimicrobials and quorum sensing systems.ConclusionsThe metagenomes show strong resemblance to activated sludge communities from a nitrogen removal reactor at a wastewater treatment plant, suggesting that natural bioremediation occurs through microbial metabolism. Elevated nitrate and rich secondary metabolite biosynthetic capacity suggest incomplete remediation and the potential for novel pharmacologically active compounds.

Project description:Microbial communities can play important role in arsenic release in groundwater aquifers. To investigate the microbial communities in high arsenic groundwater aquifers in agricultural irrigation area, 17 groundwater samples with different arsenic concentrations were collected along the agricultural drainage channels of Hangjinhouqi County, Inner Mongolia and examined by illumina MiSeq sequencing approach targeting the V4 region of the 16S rRNA genes. Both principal component analysis and hierarchical clustering results indicated that these samples were divided into two groups (high and low arsenic groups) according to the variation of geochemical characteristics. Arsenic concentrations showed strongly positive correlations with [Formula: see text] and total organic carbon (TOC). Sequencing results revealed that a total of 329-2823 operational taxonomic units (OTUs) were observed at the 97% OTU level. Microbial richness and diversity of high arsenic groundwater samples along the drainage channels were lower than those of low arsenic groundwater samples but higher than those of high arsenic groundwaters from strongly reducing areas. The microbial community structure in groundwater along the drainage channels was different from those in strongly reducing arsenic-rich aquifers of Hetao Plain and other high arsenic groundwater aquifers including Bangladesh, West Bengal, and Vietnam. Acinetobacter and Pseudomonas dominated with high percentages in both high and low arsenic groundwaters. Alishewanella, Psychrobacter, Methylotenera, and Crenothrix showed relatively high abundances in high arsenic groundwater, while Rheinheimera and the unidentified OP3 were predominant populations in low arsenic groundwater. Archaeal populations displayed a low occurrence and mainly dominated by methanogens such as Methanocorpusculum and Methanospirillum. Microbial community compositions were different between high and low arsenic groundwater samples based on the results of principal coordinate analysis and co-inertia analysis. Other geochemical variables including TOC, [Formula: see text], oxidation-reduction potential, and Fe might also affect the microbial composition.

Dataset Information

Spatiotemporal variation of nitrate concentrations in soil and groundwater of an intensely polluted agricultural area.

Publications

Spatiotemporal variation of nitrate concentrations in soil and groundwater of an intensely polluted agricultural area.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets