A Review of Current and Emerging Approaches for Water Pollution Monitoring
Abstract
:1. Introduction
2. Utilizable Elements for Wastewater Monitoring
2.1. Physicochemical Characterization in Monitoring Environmental Pollution
2.2. Spectrometry Technology in Detecting Toxicant in Wastewaters
2.3. Biomonitoring Environmental Pollution
Reliability of Bacterial Community in Waterways Biomonitoring
2.4. Molecular Approaches in Taxonomical Analyses
2.4.1. Assessment of Microbial Community Using Molecular Fingerprinting Techniques
2.4.2. Advanced High-Throughput Sequencing for Complex Taxonomic Diversity Assessment
2.5. Gene Functionality Assessment in Aquatic Environment
2.5.1. Quantification of Functional Genes Involved in the Biodegradation of Pollutants Using qRT-PCR
2.5.2. Microarray as Environmental Monitoring Tool
2.5.3. Advanced Gene Expression Profiling for Wastewater and Aquatic System Monitoring Using Metatranscriptomics Approach
2.6. Functionality Analysis of Bacterial Nucleic Acids through Flow Cytometry
3. Summary and Future Outlooks
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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NSFWQI [25] | CCME WQI [26] | OWQI [27] | MMWQI [19] | ||||
---|---|---|---|---|---|---|---|
91–100 | Excellent | 95–100 | Excellent | 90–100 | Excellent | 90–100 | Excellent |
71–90 | Good | 80–94 | Good | 85–89 | Good | 80–89 | Good |
51–70 | Medium | 60–79 | Fair | 80–84 | Fair | 50–79 | Moderate |
26–50 | Bad | 45–59 | Marginal | 60–79 | Poor | 0–49 | Poor |
0–25 | Very bad | 0–44 | Poor | 0–59 | Very poor |
(a) Industrial Wastewaters | |||||||
* Parameters | Rubber Effluent [32] | Paper Mill Effluent [33] | Textile Effluent [34] | Raw Palm Oil Mill Effluent; POME [35] | Pharmaceutical Industry Wastewater [36] | ||
pH | 5.7 ± 0.3 | 5.6–5.8 | 8.3–9.5 | 3.4–5.2 | 9.79 | ||
BOD | 1340 ± 2.0 | 5279 | 137 | 10,250–43,750 | 586.07 | ||
COD | 2834 ± 1.9 | 9507 | 278–736 | 15,000–100,000 | 1458.03 | ||
TSS | 3512 ± 4.8 | 1747 | 85–354 | 5000–54,000 | - | ||
TS | - | - | - | - | 407.67 | ||
TFS | - | - | - | - | 162.67 | ||
TVS | - | - | - | - | 384 | ||
(b) Domestic Wastewater | |||||||
* Parameters | Domestic Sewage [36] | Hospital Liquid Effluent [37] | Greywater [38] | ||||
Dish Washer | Laundry | Kitchen | |||||
pH | 7.6 | 7.45–8.85 | 10 ± 0.2 | 8.3 ± 0.8 | 6.9 ± 0.4 | ||
EC (μS/cm) | - | 152–350 | 2199 ± 753 | 653 ± 423 | 449 ± 341 | ||
BOD | 600 | 255–850 | 184.6 ± 24 | 1363 ± 950 | 831 ± 358 | ||
COD | 1006.42 | 455–879 | 411 ± 59 | 2072 ± 1401 | 1119 ± 476 | ||
TSS | - | 420–640 | 11 ± 1.3 | 169 ± 96 | 319 ± 209 | ||
VSS | - | - | 10 ± 0.5 | 139 ± 90 | 314 ± 205 | ||
TS | 888 | - | 2535 ± 1053 | 1085 ± 608 | 883 ± 426 | ||
TFS | 504 | - | - | - | - | ||
TVS | 245 | - | - | - | - | ||
NH4 + | - | 7–47.9 | - | - | - | ||
NH4+-N | - | - | 0.11 ± 0.07 | 1.4 ± 1.1 | 0.20 ± 0.26 | ||
NO2– | - | 2.1–4.2 | - | - | - | ||
NO3– | - | 23.9–56.7 | - | - | - | ||
NO3––N | - | - | 0.44 ± 0.06 | 0.68 ± 0.53 | 1.1 ± 1.2 | ||
NO2––N (μg/L) | - | - | 0.05 ± 0.01 | 75 ± 56 | 20 ± 17 | ||
TN | - | - | <0.5 | 6.2 ± 5.3 | 6.5 ± 5.0 | ||
TP | - | - | 187 ± 51 | 1.2 ± 0.81 | 2.7 ± 3.1 | ||
PO4–P | - | - | <0.05 | 0.22 ± 0.66 | 1.5 ± 2.8 | ||
(c) Agricultural Wastewater and Stormwater Runoffs | |||||||
* Parameters | Agricultural Wastewaters | Stormwater Runoffs [39] | |||||
Degraded Agricultural Watershed [40] | Swine Wastewater [41] | Catfish Pond Water [42] | Aqua-Culture Wastewater [43] | Rooftop runoff | Road Runoff in Residen-tial Area | Main Traffic Road Run-off | |
pH | - | 6.5–9 | 6.86 ± 0.06 | - | - | - | - |
EC (mS/cm) | - | 1.25–5.58 | 2.92 ± 1.1 | - | - | - | - |
DO | 7.91 ± 0.61 | 4.14–7.64 | 7.50 ± 1.10 | 2.45 ± 0.13 | - | - | - |
BOD | - | 163–3550 | 1.82 ± 0.12 | - | - | - | - |
COD | - | 210–9400 | - | 66.6 ± 6.44 | 346.92 ± 241.88 | 561.75 ± 719.30 | 570.70 ± 489.41 |
TSS | 21.13 ± 28.41 | - | 486.20 ± 11.60 | - | 43.07 ± 31.78 | 286.94 ± 187.96 | 373.77 ± 186.23 |
TDS | - | 770–648 | 602.60 ± 15. 80 | - | - | - | - |
NTU | 29.66 ± 24.5 | 0.21–3.65 | 112.5 ± 4.6 | - | - | - | - |
ISS | 15.14 ± 23.31 | - | - | - | - | - | - |
NH4+-N | - | - | - | 2.35 ± 0.56 | 23.88 ± 17.24 | 21.44 ± 22.48 | 15.29 ± 10.04 |
NO3––N | - | - | - | 0.51 ± 0.013 | 19.06 ± 12.49 | 11.41 ± 10.58 | 9.94 ± 6.98 |
NO2––N | - | - | - | 0.134 ± 0.03 | - | - | - |
TN | 0.30 ± 0.32 | - | - | 3.60 ± 1.31 | - | - | - |
TP | 0.09 ± 85.25 | - | - | 0.23 ± 0.047 | 0.11 ± 0.11 | 1.23 ± 1.73 | 0.16 ± 0.20 |
PO43− | - | 55–1680 | 1.34 ± 0.05 | - | - | - | - |
NO3 | - | 37–2730 | 2.32 ± 0.09 | - | - | - | - |
NO2 | - | 50–1427 | - | - | - | - | - |
Chloride | - | - | 24.75 ± 1.21 | - | - | - | - |
Alkalinity | - | - | 101.12 ± 2.12 | - | - | - | - |
Cu | - | - | - | - | 0.05 ± 0.06 | 0.15 ± 0.24 | 0.09 ± 0.10 |
Fe | - | - | - | - | 0.22 ± 0.36 | 0.09 ± 0.08 | 0.11 ± 0.26 |
Mn | - | - | - | - | 0.20 ± 0.33 | 0.16 ± 0.17 | 0.14 ± 0.12 |
Zn | - | - | - | - | 4.40 ± 7.52 | 0.11 ± 0.05 | 0.06 ± 0.05 |
Pb | - | - | - | - | 0.03 ± 0.03 | 0.01 ± 0.01 | 0.01 ± 0.02 |
Bioindicator(s) | Applications | Site of Study | References | |
---|---|---|---|---|
Plants (Macrophytes) | Lactuca sativa | Heavy metals in industrial effluent Polycontaminated industrial effluents | Decontamination stations’ outlet of companies in Franche-Comte’, France | [76,77] |
Oenanthe sp., Juncus sp., Typha sp., Callitriche sp. 1, Callitriche sp. 2 | Cd, Cr, Cu, Ni, Pb, Zn, and As | Storm water runoff in detention pond, northeast of Nantes, France | [78] | |
Seagrasses: Zostera muelleri, Zostera nigricaulis, Ruppia megacarpa | As, Cd, Cu, Pb, Se, and Zn | Derwent estuary in Tasmania, Australia | [79] | |
Typha latifolia (broadleaf cattail) | Trace metals (Cd, Cu, Fe, Mn, Ni, Pb, and Zn) from various types of pollutions | Different types of surface water resources in Greater Poland, upper and lower Silesia, Poland, Europe | [80] | |
Macroalgae | Red: Gracillaria sp., Pyropia columbina, Porphyra lucassi, Grateloupia turuturu Brown: Scytopsiphon lomentaria, Ecklonia radiata, Undaria pinnatifida Green: Ulva australis, Ulva compressa | As, Cd, Cu, Pb, Se, and Zn | Derwent estuary in Tasmania, Australia | [79] |
Vertebrate | Pomatoschistus microps larvae | Nitrogen contamination in estuarine ecosystem | Minho and Lima estuaries in Portugal | [81] |
Oreochromis niloticus | Metals (Cu, Zn, Mn, Cd, Pb, and Fe) | River Nile, Egypt | [82] | |
Transgenic zebrafish | Hazardous metal pollution in fresh water (Lab scale experiment) | National Taiwan University, Taipei, Taiwan | [83] | |
Invertebrate | Lamellidens marginalis | Heavy metals: Cr, Mn, Co, Ni, Cu, Zn, Se, As, Sr, Cd, Sn, Sb, Hg, and Pb | Dhimbe reservoir, Maharashtra, India | [84] |
Adult Odonata (dragonfly) | Monitoring urbanization impact on aquatic environment | Urban streams of Manaus, Amazonas, Brazil | [85] | |
Mytilus spp. | Microplastic pollution monitoring | Along Norwegian coast, Europe | [86] | |
Porifera Hymeniacidon perlevis sponge | Cu, Zn, and the hydrocarbon fluoranthene | Along Normandy coast, France | [87] | |
Nematodes community index | River water polluted by industrial, agricultural and sewage effluents | Beigang River, Taiwan | [88] | |
Plankton | Marine diatom: (Thalassiosira weissflogii) Estuarine copepod: (Acartia tonsa, Acartia tonsa nauplii) | Cu in pesticide- monitoring in aquatic system | Mondego valley, Figueira da Foz, Portugal | [89] |
Zooplanktons Chironomus, Oligochaete | Monitoring anthropogenic nitrogen impact on aquatic ecosystem | Lake Nanhu in Wuhan, Hubei Province, China | [90] | |
Daphnia longispina | S-metolachlor of pesticide- monitoring in aquatic system (lab scale experiment) | Aveiro, Portugal | [91] | |
Virus | Pepper mild mottle virus (PMMoV), human picobirna-viruses (hPBV), Torque teno virus (TTV) | Human fecal pollution in river | Along Ruhr and Rhine rivers in the North Rhine Westphalia region, Germany | [92] |
Bacteria | Fecal indicator bacteria (Enterococcus) | Sewage-contaminated groundwater | Avalon Beach, California, USA | [93] |
Bacteroidales-based biomarkers | Human and livestock wastes | Karst regions in Illinois, Wisconsin, Kentucky, Missouri, USA | [94] | |
Chromatiaceae, Alcaligenaceae | Palm oil mill effluent (POME) final discharge | River water (approximately 3 km from palm oil mill), Malaysia | [95] | |
Clostridia, Epsilonproteobacteria | Paper mill effluent | Daling River, Northeast China | [73] | |
Cupriavidusgilardii, Ralstoniapickettii | Heavy metals (Cu, Zn, Fe and Ni) | Cataño, Puerto Rico | [96] | |
Bifidobacterium spp. | Point source pollution monitoring such as from agricultural, recreation and water supply | Nanshih River, Taiwan | [44] |
Illumina Platform/Sequencer | Applications | References |
---|---|---|
Illumina HiSeq 2000 | Metagenomic analyses of microbial community pattern in an anaerobic digestion sludge of a full-scale municipal wastewater treatment plant (WWTP). | [147] |
Illumina HiSeq | Profiling of bacterial community changes in downstream as compared to upstream sections of a river receiving untreated domestic wastewater discharge. | [148] |
Illumina HiSeq 2500 | Characterization of bacterial community patterns due to seasonal changes in anthropogenically disturbed rivers. | [149] |
Illumina HiSeq 2500 | Analysis of structures and functions of river water and sediment microbial community during aerobic and anaerobic conditions of endocrine disruptor (17β-estradiol) biodegradation. | [150] |
Illumina MiSeq | Determination of potential bioindicator from bacterial community obtained from a palm oil mill effluent (POME) treatment system and the receiving waterway. | [95] |
Illumina MiSeq | Determination of pathogenic bacteria within the bacterial community utilizable for risk assessment of urban surface water. | [151] |
Illumina MiSeq | A study on the impact of surface water introduction on the microbial community diversity and function within a spring pool in a cave influenced by seasonal changes factor. | [152] |
Illumina MiSeq | Assessment of the unique presence of targeted bacterial indicator (Alcaligenaceae and Chromatiaceae) within the microbial community in POME final discharge polluted rivers as compared to other non-POME polluted streams. | [153] |
Sample | Site | Platform | Reference |
---|---|---|---|
Activated sludge | Wastewater Treatment Plant (WWTP), Hong Kong | Illumina Hi-Seq2000 | [185] |
Wastewater wet oxidation effluents | Rovereto, Italy | Illumina Hi-Seq2000 | [179] |
Deep seafloor sediment | Gulf of Mexico | Illumina Hi-Seq2500 | [186] |
Deep seawater | Northeast Pacific Ocean | Roche GS FLX Titanium chemistry pyrosequencing | [187] |
River water | Amazon River, South America | Illumina Hi-Seq2500 | [188] |
River and seawater | Columbia River, estuary and plume | Illumina Hi-Seq1000 | [189] |
Biofilm | Tamagawa River, Japan | Illumina MiSeq | [190] |
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Zolkefli, N.; Sharuddin, S.S.; Yusoff, M.Z.M.; Hassan, M.A.; Maeda, T.; Ramli, N. A Review of Current and Emerging Approaches for Water Pollution Monitoring. Water 2020, 12, 3417. https://doi.org/10.3390/w12123417
Zolkefli N, Sharuddin SS, Yusoff MZM, Hassan MA, Maeda T, Ramli N. A Review of Current and Emerging Approaches for Water Pollution Monitoring. Water. 2020; 12(12):3417. https://doi.org/10.3390/w12123417
Chicago/Turabian StyleZolkefli, Nurhasliza, Siti Suhailah Sharuddin, Mohd Zulkhairi Mohd Yusoff, Mohd Ali Hassan, Toshinari Maeda, and Norhayati Ramli. 2020. "A Review of Current and Emerging Approaches for Water Pollution Monitoring" Water 12, no. 12: 3417. https://doi.org/10.3390/w12123417
APA StyleZolkefli, N., Sharuddin, S. S., Yusoff, M. Z. M., Hassan, M. A., Maeda, T., & Ramli, N. (2020). A Review of Current and Emerging Approaches for Water Pollution Monitoring. Water, 12(12), 3417. https://doi.org/10.3390/w12123417