Citation: Mathieu F. M. Cellier. Evolutionary analysis of Slc11 mechanism of proton-coupled metal-ion transmembrane import[J]. AIMS Biophysics, 2016, 3(2): 286-318. doi: 10.3934/biophy.2016.2.286
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Water is crucial for sustaining life on the earth. However, the ongoing uses making it contaminated, resulting in the production of a large amount of wastewater. Wastewater encompasses liquid wastes discharged from domestic residences, commercial properties, industry, and/or agricultural activities and can contain a wide range of potential contaminants. In the most common usage, it refers to the municipal wastewater that contains a broad spectrum of contaminants resulting from the mixing of wastewaters from different sources [1]. Based on the different factors (for example, socio-economic, water usage, the degree of infiltration, nature, and magnitude of industrial content etc.) municipal wastewaters cannot be considered as a unique group of effluents [2]. However, the main component of wastewater is water (about 99.9%) and the rest are suspended and dissolved organic solids (carbohydrates, lignin, fats, soaps, synthetic detergents, synthetic organic chemicals from the process industries), inorganic solids including metals and also pathogenic viruses and bacteria [3]. These pollutants could exhibit toxic effects on aquatic life and the public leading to a less suitable environment, poor health, a less flourishing economy, and ultimately, a poor quality of life [4]. Therefore, it is necessary to know details about different physico-chemical properties such as temperature, pH, electrical conductivity, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total solids (TS), total suspended solids (TSS), and total dissolved solids (TDS) which are used for evaluating wastewater quality.
Although waste management strategies adopted in Bangladesh, it has failed to keep pace with the industrial growth and urbanization. Several studies have documented in Bangladesh on physico-chemical properties of wastewater.
Recently, Ali et al. [5] have investigated physico-chemical properties of wastewater from industries in Chittagong whereas; Hasan et al. [3] have investigated sewage wastewater for physico-chemical parameters in Pagla sewage plant, Dhaka. In addition, physico-chemical properties and heavy metals in industrial wastewater from Kushtia industrial area [6], physico-chemical parameters of tannery effluent in Dhaka [7], effluents of leather industries in Hazaribagh area of Dhaka city [8], and effects of physico-chemical properties of textile dyeing effluents on the surface water quality in the Dhaka-Narayanganj-Demra industrial area [9] have been studied in Bangladesh. Furthermore, a review study on textile wastewater in Bangladesh during 2005–2014 has been documented [10].
In Bangladesh, there is a little study on wastewater comprising a large study area. It may be due to the high population density and high industrial activities. The studies investigated only the quality of wastewater and simply compared with the standard values. Nevertheless, the source of physico-chemical parameters has investigated there is a lack of statistical analysis. At the same time, most of the studies in this field have focused on big cities and industrial areas rather than sub-urban and residential areas.
Therefore, the objectives of this research were aimed to determine the physico-chemical characteristics of municipal wastewater effluent from five sewage points of Jhenaidah town (a sub-urban area) and to investigate the correlation among them using Pearson's correlation analysis.
The wastewater samples were collected from the municipality areas of Jhenaidah district, which is situated in the southwestern part of Bangladesh under Khulna division. The district geographically is situated between latitudes of 23˚15'N to 23˚45'N and between longitude of 88˚45'E to 89˚15'N. The district is bordered by Kushtia district to the north, Jessore and 24 Parganah of West Bengal (India) to the south, Magura and partly of Rajbari district to the east, Chuadanga and 24 Parganah of West Bengal (India) to the west. The sampling locations and study areas are indicated in Figure 1.
The experimental method involved the collection of raw samples in clean plastic high density polyethylene (HDPE) containers of 3 liters capacity at five different locations inlet chamber, drainage, and sewage line. Samples were collected from the depth of 6 inches below the water surface in a thoroughly cleaned HDPE container of 3 liters capacity of each provided with the cap in February 2016. A total of 70 samples were collected in the morning and evening time for seven consecutive days. The collected samples were homogenized for each location. Prior to collecting samples, the containers were cleaned by 25% (w/v) HNO3 and rinsed several times with distilled water. The preservation procedure includes keeping the samples in the dark, adding a chemical preservative, lowering the temperature to retard reactions or combinations of these.
The physico-chemical analysis was implemented according to the standard methods of analysis of each parameter. Each of them is described below in details.
pH of the wastewater samples was measured using a digital pH meter (model BT-600, BOECO, Germany) whereas, Electrical Conductivity (EC) was measured using an electrical conductivity meter (CM-230). Standard procedures were maintained to avoid false reading and contamination. pH and EC were measured during collection of samples at each sampling points.
The total solids (mg/L) of the samples were determined as described by Punmia and Jain [11]. In brief, a cleaned dish was taken and ignited to constant weight (W1) on which 25 mL of well-mixed sample was transferred. Then the sample evaporated to dryness at 103 ℃ for 24 hours, in a constant temperature oven. After cooling the dish in a desiccator the weight was determined (W2). Then total solids content was calculated as follows:
$ {\rm{Total}}\;{\rm{Solid}}\;{\rm{(TS)}} = \frac{{{{\rm{W}}_{\rm{2}}} - {{\rm{W}}_{\rm{1}}}}}{{\rm{V}}} \times 40 $ | (1) |
Where V is the volume of the sample.
In addition, the total suspended solids (mg/L) were determined according to the method described by Punmia and Jain [11]. In brief, a cleaned crucible with filter paper was ignited to constant weight in an electric oven (W1). Then 25 mL sample was taken and filtered through the crucible. After that, the crucible was dried in an oven at 103 ℃ for 24 hours; cooled in a dedicator and weighed out (W2). The total suspended solids were then calculated using the following formula:
$ {\rm{Total}}\;{\rm{Suspended}}\;{\rm{Solids}}\;{\rm{(TSS)}} = \frac{{{{\rm{W}}_{\rm{2}}} - {{\rm{W}}_{\rm{1}}}}}{{\rm{V}}} \times 40 $ | (2) |
Another physical parameter; the total dissolved solids (mg/L) were determined by evaporating the waste samples to dryness [12]. In this method, 50 mL of sample were transferred to a weighed evaporating dish and evaporated to dryness by heating for 1–2 hours at 180 ℃ to a constant weight. Total dissolved solids were calculated as follows:
$ {\rm{Total}}\;{\rm{Dissolved}}\;{\rm{Solids}}\;{\rm{(TDS)}} = \frac{{{\rm{mg}}\;{\rm{of}}\;{\rm{residue}}}}{{{\rm{mL}}\;{\rm{of}}\;{\rm{sample}}}} \times 1000 $ | (3) |
COD was measured using to the American Public Health Association (APHA) standard closed reflux titrimetric method [13]. Potassium dichromate (0.01667M K2Cr2O7) and sulphuric acid (98% H2SO4) solution was added to water sample taken in culture tube. Then it was refluxed for two hours at 150 ℃. The digested solution was transferred to conical flask to cool at room temperature and titrated with ferrous ammonium sulphate (FAS) solution (0.025M) using 2 drops ferroin indicator until color changed from blue-green to radish-brown.
$ {\rm{COD}}\;{\rm{as}}\;{\rm{mg}}\;{\rm{of}}\;{{\rm{O}}_{\rm{2}}}{\rm{/L = }}{\textstyle{{{\rm{(A}} - {\rm{B)}} \times {\rm{M}} \times {\rm{8000}}} \over {{\rm{mL}}\;{\rm{of}}\;{\rm{sample}}}}} $ | (4) |
Here, A = mL of FAS used for blank; B = mL of FAS used for sample; M = Molarity of FAS; 8000 = milliequivalent weight of oxygen × 1000 mL/L.
BOD was determined by 5-day testing method according to the APHA standard method [13]. In brief, two 100 mL bottles were obtained with lid and cleaned thoroughly with distilled water. Then, 25 mL sample was taken in each bottle and distilled water was added into each bottle to make the total volume 100 mL. Then the two bottles were closed tight enough with the lid. 10 mL of manganese sulfate (MnSO4) solution and 2 mL of alkali-azide solution were added into the both bottle. Then the bottle was closed and mixed properly by inverting the bottle several times. When the precipitate settled down leaving a clear supernatant above the precipitate, it was agitated again slowly by inverting the bottle as described above. When the settling had completed, 8 mL of concentrated phosphoric acid was added into the suspension. The bottle was closed and mixed by gentle inversion until dissolution was completed. Finally, 100 mL of the aqueous sample was taken into a conical flask and titrated with 0.05 M Na2S2O3 solution until a pale yellow solution is reached. After that, 2 mL of freshly prepared starch solution was added into the conical flask and titration was continued until a blue color appeared. At the same time, one sample was kept at 20 ℃ in an incubator for 5 days containing MnSO4, alkali-azide and phosphoric acid solution. After 5 day, the same method was followed for the 5 day incubated samples. Blank titration was carried out using distilled water instead of wastewater. BOD5d (at 20 ºC) was calculated using the following equation:
$ {\rm{BO}}{{\rm{D}}_{{\rm{5d}}}}\;{\rm{as}}\;{\rm{mg}}\;{\rm{of}}\;{{\rm{O}}_{\rm{2}}}{\rm{/L = 16}}\;{\rm{(V1}} - {\rm{V2)}} $ | (5) |
Where V1 = mL of Na2S2O3 used for the sample before incubation (at day 1); V2 = mL of Na2S2O3 used for the sample after 5 day incubation (at 5 day).
Calcium hardness was determined through adding 1 mL of 1N NaOH solution and 0.1 mg of murexide P indicator into 50 ml of the water sample and finally titrated with standard 0.02N EDTA solution until the red color of the solution turned to purple. Calcium hardness was calculated from the equation:
$ {\rm{Hardness}}\;{\rm{as}}\;{\rm{CaCO_3}} = \frac{{{\rm{Burette}}\;{\rm{reading}}}}{{{\rm{Sample}}\;{\rm{taken}}\;{\rm{in}}\;{\rm{mL}}}} \times 1000 $ | (6) |
Descriptive statistics and Pearson correlation coefficient (p-value of < 0.05 was considered to indicate statistical significance were performed using the open source statistical software R (Version 3.4.3).
The physico-chemical parameters of the wastewater in the Jhenaidah municipality area have been summarized in Table 1.
Parameters | Units | Minimum | Maximum | Mean | Standard Deviation | Standard Value [14] |
pH | - | 6.72 | 7.60 | 7.17 | 0.32 | 6–9 |
EC | S/cm | 0.0015 | 0.0085 | 0.0036 | 0.0028 | 0.0012 |
Hardness as CaCO3 | mg/L | 422 | 745 | 585.20 | 126.08 | N/A |
TS | mg/L | 218.00 | 327.20 | 256.16 | 42.15 | N/A |
TSS | mg/L | 184 | 258 | 212 | 27.68 | 150–500 |
TDS | mg/L | 72 | 173 | 110.40 | 39.09 | 2100 |
DO | mg/L | 0.40 | 2.83 | 1.05 | 1.01 | 4.5–8 |
BOD5d (at 20 ℃) | mg/L | 16 | 71 | 48.60 | 21.87 | 50 (inland surface)–250 (irrigated land) |
COD | mg/L | 69 | 269 | 175.4 | 74.59 | 200 (inland surface)–400 (irrigated land) |
pH is a measure of the acidity or alkalinity of water and is one of the stable measurements to evaluate wastewater quality. It is a simple parameter but is extremely important, since most of the chemical reactions in the aquatic environment are controlled by any change in its value. In addition, aquatic organisms are sensitive to pH changes and biological treatment requires pH control or monitoring. Moreover, the toxicity of heavy metals also is enhanced at particular pH. Thus, pH is having primary importance in deciding the quality of wastewater effluent. Waters with a pH value of about 10 are exceptional and may reflect contamination by a strong base such as NaOH and Ca(OH)2. The pH of wastewater in this study varies between 6.72 and 7.60 with an average value of 7.17. These values are within the standard limit (6.0–9.0) of pH in wastewater in Bangladesh [14].
All the samples surpassed the lower limit but below the upper limit of pH (Figure 2a). A study of municipal wastewater in Sylhet [15] showed almost the same results of pH (Table 2), whereas, wastewater from industrial areas in Bangladesh is different from this study. Industrial wastewater shows pH as low as 3.9 to as high as 14 in different industrial areas [10]. This can be attributed that the wastewater from industries is more polluted than municipal wastewater in terms of pH.
Study Area | pH | EC (s/cm) | Hardness as CaCO3 (mg/L) | TS (mg/L) | TSS (mg/L) | TDS (mg/L) | DO (mg/L) | BOD (mg/L) | COD (mg/L) | Reference |
Jhenaidah (n = 70) | 7.17 (6.72–7.60) | 0.0036 (0.0015–0.0085) | 585.20 (422–745) | 256.16 (218–327.20) | 212 (184–258) | 110.40 (72–173) | 1.05 (0.40–2.83) | 48.60 (16–71) | 175.40 (69–269) | This Study |
Sylhet City (n = 6) | 6.24–6.89 | - | - | 450 (300–600) | 150 (100–200) | 300 (200–400) | 40 (0–80) | 138 (108–168) | - | [15] |
Narayanganj (n = 9) | 9.88 | 0.014 (0.001–0.06) | - | - | 1123.11 (736–1960) | 9123.78 (791–46700) | 2.36 (0.42–4.6) | 573.89 (415–770) | 1223.33 (860–1560) | [9] |
Hazaribag, Dhaka (n = 10) | 7.19 | 0.006 | - | - | 1613 | 3455 | 1.74 | 548.50 | 1441 | [8] |
Hazaribag, Dhaka (n = 12) | 6.56 | 0.005 | - | - | 2048.42 | 3516.50 | 1.58 | 754.50 | 2776.83 | [7] |
Different areas of Bangladesh | 3.9–14 | 0.00025–0.064 | - | - | 24.9–39.50 | 90.7–5980 | 0–7 | 10–786 | 41–2430 | [10] |
Kushtia (n = 8) | 4.19 –8.49 | 0.00084–0.003 | 485–848 | - | - | - | - | 57–88 | 150–108 | [6] |
Chittagong (n = 10) | 8.23 (6.80–10.21) | 0.004 (0.0005–0.025) | 78.02 (9.20 –192) | - | 186.50 (16–480) | 2577.90 (233–12570) | 1.72 (0.1–3.6) | 155.59 (12.40–276.90) | 507.54 (145.47 –1854.60) | [5] |
The hardness of wastewater is the amount of dissolved calcium and magnesium in it. In this study, hardness due to calcium was estimated. It ranged between 422 and 745 mg/L with an average value of 585.2 mg/L. Currently, there is no standard value for the hardness of wastewater in Bangladesh. Recently, Islam et al. [6] have found hardness 485–848 mg/L from the industrial wastewater in Kushtia that is similar to this study. On the contrary, hardness as CaCO3 in industrial wastewater from Chittagong ranges between 9.20–192 mg/L [5], which is lower than this study.
The parameter of EC is an indication of the concentration of TDS and major ions in a given water body, whereas the parameters TS, TSS, and TDS refer mainly to inorganic substances dissolved in water [5]. Electrical Conductivity (S/cm) is the capacity of water to carry an electrical current and varies both with the number and with types of ions in the solutions, which in turn is related to the concentration of ionized substances in the water. The mean value of EC in this study was found 0.0036 S/cm, which is three times higher than the standard value (0.0012 S/cm) (Table 1). All the samples exceeded the standard limit of EC (Figure 2b). This value is similar to the most of the findings in Bangladesh; on the contrary, wastewater from Narayanganj industrial areas shows 0.014 S/cm. This value of electrical conductivity is much higher than the acceptable limit and may be due to the heavy industrial activities in this area. TS ranges from 218.00–327.20 mg/L (256.16 mg/L by average), which is almost half of Alam et al. [15]. Currently, there is no standard value for TS in Bangladesh. The mean values of TSS and TDS in the study area are 212 mg/L and 110.40 mg/L respectively.
TSS exceeded the lower standard value 150 mg/L, whereas, it was with in the upper standard limit (500 mg/L) (Figure 2d) [14]. In addition, TSS value in this study is higher than municipality wastewater from Sylhet [15] and industrial wastewater from Chittagong [5]. Although, other studies of industrial wastewater from Hazaribagh and Narayanganj show the higher value of TSS (Table 2). In this study, TSS in wastewater samples higher than 200 mg/L, which is considered strong wastewater and therefore should not be discharged into environment [16]. Relatively higher value of TSS in this study area is due to small-scale industrial activities. The values of TDS in the wastewater were between 72–173 mg/L. It is less than the standard value of 2100 mg/L [14] and all the samples have relatively lower values than the standard one (Figure 2e). Therefore, it is considered desirable before discharging into the surface water bodies.
Dissolved oxygen (DO) is an essential element in the biological treatment process for aerobic bacteria. Therefore, higher values of DO are desirable in wastewater to decompose organic contents into it. There is an inverse relationship between DO and BOD5d (at 20 ºC), i.e., when DO is minimum BOD5d (at 20 ºC) rates are higher [5]. The DO in wastewater in this study was found at 1.05 mg/L, which is lower the standard value 4.5–8.0 mg/L (Table 1). All the samples contain a very low amount of DO. Although it is desired to have a sufficient amount of DO in order to oxidize the organic content properly. Industrial wastewater from different areas of Bangladesh shows similar value to this study except for municipality wastewater from Sylhet city contains as high as 80 mg/L (Table 2). BOD5d (at 20 ºC) in wastewater from Jhenaidah municipality area ranges between 16 and 71 with an average value of 48.60 mg/L. It is less than the standard value of 50 mg/L (inland surface) and 250 mg/L (irrigated land). Two samples (S2 and S5) have values higher than the standard limit for discharging into the inland surface water system (Figure 2g). Nevertheless, all the samples are appropriate for discharging into irrigated land in terms of BOD5d (at 20 ºC). This finding is similar to industrial wastewater from Kushtia [6], but lower than the other studies in Bangladesh (Table 2).
COD is an indirect measurement of organic content in wastewater [17]. In this study, COD ranged between 69 and 269 mg/L with an average value of 175.4 mg/L. This mean value is lower than the prescribed limit 200 mg/L (inland water) and 400 mg/L (irrigated land) [14]. Although two samples (S2 and S5) have surpassed the lower limit (Figure 2h). High level of BOD5d (at 20 ºC) and COD is due to the presence of chemicals that may be organic or inorganic caused by the inflow of domestic, livestock and industrial waste that contains elevated levels of organic pollutants [16].
Pearson's correlation analysis was performed in order to analyze correlations between different physico-chemical parameters analyzed in this study. Results of correlation analysis and their corresponding trend lines are presented in Figure 3.
It represents the intercorrelations among the measured physico-chemical parameters. From Figure 3, it is apparent that TS with TSS (r = 0.99) and BOD5d (at 20 ºC) with COD (r = 0.98) show a strong positive correlation. In contrast, pH with EC (r = -0.89) and DO with BOD5d (at 20 ºC) and COD (r = -0.92, -0.89 respectively) indicate a strong but negative correlation. Other parameters such as pH, EC, TS, TSS, TDS, Hardness as CaCO3, BOD5d (at 20 ºC), and COD show a moderate correlation with each other. The negative correlation among DO, BOD5d at 20ºC and COD establish the inverse relationship between them. In contrast, a positive correlation between TS and TSS indicate that TS is largely dependent on TSS rather than TDS.
This study was set out to measure and assess the physico-chemical characteristics of municipal wastewater effluents from five sewage points of Jhenaidah town along with to investigate the correlation among them using Pearson's correlation analysis. This experiment confirmed that some wastewater parameters like pH, TDS, BOD5d (at 20 ºC), and COD meet the standard acceptable limit in Bangladesh. On the other hand, DO showed very lower values than the standard limit. TSS surpassed the acceptable limit, whereas hardness and TS showed a moderate value, which was not comparable due to the lack of standard values. Pearson's correlation analysis affirmed the inverse relation between DO and BOD5d (at 20 ºC). In addition, it confirmed the large dependency of TS on TSS rather than TDS. Therefore, the results of this study indicated a moderate quality of wastewater in Jhenaidah municipality area. The effluent could be posed a health and environmental risk to the communities rely on the receiving water, in particular to the flora and fauna, and finally the human being. So, this study recommended the further development and the necessary steps to take before discharging this wastewater into the receiving ponds, lakes, canals, rivers, and agricultural fields.
Authors are thankful to the authority of Islamic University, Kushtia 7003, Bangladesh, for providing the laboratory facilities. Furthermore, we are also thankful for the kind help from the members of the Department of Applied Chemistry and Chemical Engineering, Islamic University, Kushtia 7003, Bangladesh, during the field based wastewater sampling.
All authors declare no conflicts of interest in this paper.
[1] |
Hediger MA, Clemencon B, Burrier RE, et al. (2013) The ABCs of membrane transporters in health and disease (SLC series): introduction. Mol Aspects Med 34: 95–107. doi: 10.1016/j.mam.2012.12.009
![]() |
[2] |
Illing AC, Shawki A, Cunningham CL, et al. (2012) Substrate profile and metal-ion selectivity of human divalent metal-ion transporter-1. J Biol Chem 287: 30485–30496. doi: 10.1074/jbc.M112.364208
![]() |
[3] | Cellier MF (2013) Cell-Type Specific Determinants of NRAMP1 Expression in Professional Phagocytes. Biology (Basel) 2: 233–283. |
[4] |
Braasch I, Gehrke AR, Smith JJ, et al. (2016) The spotted gar genome illuminates vertebrate evolution and facilitates human-teleost comparisons. Nat Genet 48: 427–437. doi: 10.1038/ng.3526
![]() |
[5] |
Cellier MF, Courville P, Campion C (2007) Nramp1 phagocyte intracellular metal withdrawal defense. Microbes Infect 9: 1662–1670. doi: 10.1016/j.micinf.2007.09.006
![]() |
[6] |
Vidal SM, Malo D, Vogan K, et al. (1993) Natural resistance to infection with intracellular parasites: isolation of a candidate for Bcg. Cell 73: 469–485. doi: 10.1016/0092-8674(93)90135-D
![]() |
[7] |
Peracino B, Wagner C, Balest A, et al. (2006) Function and mechanism of action of Dictyostelium Nramp1 (Slc11a1) in bacterial infection. Traffic 7: 22–38. doi: 10.1111/j.1600-0854.2005.00356.x
![]() |
[8] |
Ehrnstorfer IA, Geertsma ER, Pardon E, et al. (2014) Crystal structure of a SLC11 (NRAMP) transporter reveals the basis for transition-metal ion transport. Nat Struct Mol Biol 21: 990–996. doi: 10.1038/nsmb.2904
![]() |
[9] |
Vastermark A, Wollwage S, Houle ME, et al. (2014) Expansion of the APC superfamily of secondary carriers. Proteins 82: 2797–2811. doi: 10.1002/prot.24643
![]() |
[10] |
Yamashita A, Singh SK, Kawate T, et al. (2005) Crystal structure of a bacterial homologue of Na+/Cl--dependent neurotransmitter transporters. Nature 437: 215–223. doi: 10.1038/nature03978
![]() |
[11] |
Forrest LR (2015) Structural Symmetry in Membrane Proteins. Annu Rev Biophys 44: 311–337. doi: 10.1146/annurev-biophys-051013-023008
![]() |
[12] |
Cellier MF (2012) Nutritional immunity: homology modeling of Nramp metal import. Adv Exp Med Biol 946: 335–351. doi: 10.1007/978-1-4614-0106-3_19
![]() |
[13] |
Shi Y (2013) Common Folds and Transport Mechanisms of Secondary Active Transporters. Annu Rev Biophys 42: 51–72. doi: 10.1146/annurev-biophys-083012-130429
![]() |
[14] |
Kowalczyk L, Ratera M, Paladino A, et al. (2011) Molecular basis of substrate-induced permeation by an amino acid antiporter. Proc Natl Acad Sci USA 108: 3935–3940. doi: 10.1073/pnas.1018081108
![]() |
[15] |
Krishnamurthy H, Piscitelli CL, Gouaux E (2009) Unlocking the molecular secrets of sodium-coupled transporters. Nature 459: 347–355. doi: 10.1038/nature08143
![]() |
[16] |
Forrest LR, Kramer R, Ziegler C (2011) The structural basis of secondary active transport mechanisms. Biochim Biophys Acta 1807: 167–188. doi: 10.1016/j.bbabio.2010.10.014
![]() |
[17] |
Faham S, Watanabe A, Besserer GM, et al. (2008) The crystal structure of a sodium galactose transporter reveals mechanistic insights into Na+/sugar symport. Science 321: 810–814. doi: 10.1126/science.1160406
![]() |
[18] |
Weyand S, Shimamura T, Yajima S, et al. (2008) Structure and molecular mechanism of a nucleobase-cation-symport-1 family transporter. Science 322: 709–713. doi: 10.1126/science.1164440
![]() |
[19] |
Ressl S, Terwisscha van Scheltinga AC, Vonrhein C, et al. (2009) Molecular basis of transport and regulation in the Na(+)/betaine symporter BetP. Nature 458: 47–52. doi: 10.1038/nature07819
![]() |
[20] |
Gao X, Lu F, Zhou L, et al. (2009) Structure and mechanism of an amino acid antiporter. Science 324: 1565–1568. doi: 10.1126/science.1173654
![]() |
[21] |
Shaffer PL, Goehring A, Shankaranarayanan A, et al. (2009) Structure and mechanism of a na+-independent amino Acid transporter. Science 325: 1010–1014. doi: 10.1126/science.1176088
![]() |
[22] | Fang Y, Jayaram H, Shane T, et al. (2009) Structure of a prokaryotic virtual proton pump at 3.2 A resolution. Nature 460: 1040–1043. |
[23] |
Gao X, Zhou L, Jiao X, et al. (2010) Mechanism of substrate recognition and transport by an amino acid antiporter. Nature 463: 828–832. doi: 10.1038/nature08741
![]() |
[24] |
Tang L, Bai L, Wang WH, et al. (2010) Crystal structure of the carnitine transporter and insights into the antiport mechanism. Nat Struct Mol Biol 17: 492–496. doi: 10.1038/nsmb.1788
![]() |
[25] |
Shimamura T, Weyand S, Beckstein O, et al. (2010) Molecular basis of alternating access membrane transport by the sodium-hydantoin transporter Mhp1. Science 328: 470–473. doi: 10.1126/science.1186303
![]() |
[26] |
Schulze S, Koster S, Geldmacher U, et al. (2010) Structural basis of Na(+)-independent and cooperative substrate/product antiport in CaiT. Nature 467: 233–236. doi: 10.1038/nature09310
![]() |
[27] |
Krishnamurthy H, Gouaux E (2012) X-ray structures of LeuT in substrate-free outward-open and apo inward-open states. Nature 481: 469–474. doi: 10.1038/nature10737
![]() |
[28] |
Ma D, Lu P, Yan C, et al. (2012) Structure and mechanism of a glutamate-GABA antiporter. Nature 483: 632–636. doi: 10.1038/nature10917
![]() |
[29] |
Khafizov K, Perez C, Koshy C, et al. (2012) Investigation of the sodium-binding sites in the sodium-coupled betaine transporter BetP. Proc Natl Acad Sci USA 109: E3035–E3044. doi: 10.1073/pnas.1209039109
![]() |
[30] | Perez C, Faust B, Mehdipour AR, et al. (2014) Substrate-bound outward-open state of the betaine transporter BetP provides insights into Na+ coupling. Nat Commun 5: 4231. |
[31] |
Malinauskaite L, Quick M, Reinhard L, et al. (2014) A mechanism for intracellular release of Na+ by neurotransmitter/sodium symporters. Nat Struct Mol Biol 21: 1006–1012. doi: 10.1038/nsmb.2894
![]() |
[32] |
Chaloupka R, Courville P, Veyrier F, et al. (2005) Identification of functional amino acids in the Nramp family by a combination of evolutionary analysis and biophysical studies of metal and proton cotransport in vivo. Biochemistry 44: 726–733. doi: 10.1021/bi048014v
![]() |
[33] |
Gaucher EA, Gu X, Miyamoto MM, et al. (2002) Predicting functional divergence in protein evolution by site-specific rate shifts. Trends Biochem Sci 27: 315–321. doi: 10.1016/S0968-0004(02)02094-7
![]() |
[34] | Knudsen B, Miyamoto MM, Laipis PJ, et al. (2003) Using Evolutionary Rates to Investigate Protein Functional Divergence and Conservation. A case study of the carbonic anhydrases. Genetics 164: 1261–1269. |
[35] | Gu X, Vander Velden K (2002) DIVERGE: phylogeny-based analysis for functional-structural divergence of a protein family. Bioinformatics 18: 500–501. |
[36] | Gu X, Zou Y, Su Z, et al. (2013) An update of DIVERGE software for functional divergence analysis of protein family. Mol Biol Evol 30: 1713–1719. |
[37] |
Echave J, Spielman SJ, Wilke CO (2016) Causes of evolutionary rate variation among protein sites. Nat Rev Genet 17: 109–121. doi: 10.1038/nrg.2015.18
![]() |
[38] |
Zhang J, Yang JR (2015) Determinants of the rate of protein sequence evolution. Nat Rev Genet 16: 409–420. doi: 10.1038/nrg3950
![]() |
[39] |
Sandler I, Zigdon N, Levy E, et al. (2014) The functional importance of co-evolving residues in proteins. Cell Mol Life Sci 71: 673–682. doi: 10.1007/s00018-013-1458-2
![]() |
[40] | de Juan D, Pazos F, Valencia A (2013) Emerging methods in protein co-evolution. Nat Rev Genet 14: 249–261. |
[41] |
Shin JH, Wakeman CA, Goodson JR, et al. (2014) Transport of magnesium by a bacterial nramp-related gene. PLoS Genet 10: e1004429. doi: 10.1371/journal.pgen.1004429
![]() |
[42] |
Courville P, Urbankova E, Rensing C, et al. (2008) Solute carrier 11 cations symport requires distinct residues in transmembrane helices 1 and 6. J Biol Chem 283: 9651–9658. doi: 10.1074/jbc.M709906200
![]() |
[43] |
Cellier MF (2012) Nramp: from sequence to structure and mechanism of divalent metal import. Curr Top Membr 69: 249–293. doi: 10.1016/B978-0-12-394390-3.00010-0
![]() |
[44] | Richer E, Courville P, Cellier M (2004) Molecular Evolutionary Analysis of the Nramp Family. Cellier M. and Gros P. (eds) Molecular biology intelligence unit. 178–194. Springer. |
[45] |
Richer E, Courville P, Bergevin I, et al. (2003) Horizontal gene transfer of "prototype" Nramp in bacteria. J Mol Evol 57: 363–376. doi: 10.1007/s00239-003-2472-z
![]() |
[46] | Jenuth JP (2000) The NCBI. Publicly available tools and resources on the Web. Methods Mol Biol 132: 301–312. |
[47] | Biegert A, Mayer C, Remmert M, et al. (2006) The MPI Bioinformatics Toolkit for protein sequence analysis. Nucleic Acids Res 34: W335–W339. |
[48] | Tamura K, Peterson D, Peterson N, et al. (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 28: 2731–2739. |
[49] | Thompson JD, Gibson TJ, Higgins DG (2002) Multiple sequence alignment using ClustalW and ClustalX. Curr Protoc Bioinform Chapter 2: Unit 2.3.: Unit. |
[50] | Gouy M, Guindon S, Gascuel O (2010) SeaView Version 4: A Multiplatform Graphical User Interface for Sequence Alignment and Phylogenetic Tree Building. Mol Biol Evol 27: 221–224. |
[51] | Burki F (2014) The Eukaryotic Tree of Life from a Global Phylogenomic Perspective. Cold Spring Harbor Perspectives in Biology 6. a016147. |
[52] |
Koonin EV (2010) The origin and early evolution of eukaryotes in the light of phylogenomics. Genome Biol 11: 209. doi: 10.1186/gb-2010-11-5-209
![]() |
[53] |
Adl SM, Simpson AGB, Lane CE, et al. (2012) The Revised Classification of Eukaryotes. J Eukaryot Microbiol 59: 429–493. doi: 10.1111/j.1550-7408.2012.00644.x
![]() |
[54] |
Lin Z, Fernandez-Robledo JA, Cellier MF, et al. (2011) The natural resistance-associated macrophage protein from the protozoan parasite Perkinsus marinus mediates iron uptake. Biochemistry 50: 6340–6355. doi: 10.1021/bi200343h
![]() |
[55] | Guindon S, Dufayard JF, Lefort V, et al. (2010) New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol 59: 307–321. |
[56] |
Shih AC, Lee DT, Peng CL, et al. (2007) Phylo-mLogo: an interactive and hierarchical multiple-logo visualization tool for alignment of many sequences. BMC Bioinformatics 8: 63. doi: 10.1186/1471-2105-8-63
![]() |
[57] |
Stansfeld PJ, Goose JE, Caffrey M, et al. (2015) MemProtMD: Automated Insertion of Membrane Protein Structures into Explicit Lipid Membranes. Structure 23: 1350–1361. doi: 10.1016/j.str.2015.05.006
![]() |
[58] | DeLano WL (2002) The PyMOL Molecular Graphics System. DeLano Scientific, San carlos, california, USA. Available from: http: //www.pymol.org. |
[59] | Ye Y, Godzik A (2003) Flexible structure alignment by chaining aligned fragment pairs allowing twists. Bioinformatics 19 Suppl 2: ii246–ii255. |
[60] | Pieper U, Webb BM, Barkan DT, et al. (2011) ModBase, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids Res 39: D465–D474. |
[61] |
Narunsky A, Nepomnyachiy S, Ashkenazy H, et al. (2015) ConTemplate Suggests Possible Alternative Conformations for a Query Protein of Known Structure. Structure 23: 2162–2170. doi: 10.1016/j.str.2015.08.018
![]() |
[62] |
Czachorowski M, Lam-Yuk-Tseung S, Cellier M, et al. (2009) Transmembrane Topology of the Mammalian Slc11a2 Iron Transporter. Biochemistry 48: 8422–8434. doi: 10.1021/bi900606y
![]() |
[63] |
Xia J, Yamaji N, Kasai T, et al. (2010) Plasma membrane-localized transporter for aluminum in rice. Proc Natl Acad Sci USA 107: 18381–18385. doi: 10.1073/pnas.1004949107
![]() |
[64] |
Pottier M, Oomen R, Picco C, et al. (2015) Identification of mutations allowing Natural Resistance Associated Macrophage Proteins (NRAMP) to discriminate against cadmium. Plant J 83: 625–637. doi: 10.1111/tpj.12914
![]() |
[65] |
Tavoulari S, Margheritis E, Nagarajan A, et al. (2016) Two Na+ Sites Control Conformational Change in a Neurotransmitter Transporter Homolog. J Biol Chem 291: 1456–1471. doi: 10.1074/jbc.M115.692012
![]() |
[66] | Ashkenazy H, Erez E, Martz E, et al. (2010) ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Res 38: W529–W533. |
[67] |
De Falco L, Bruno M, Andolfo I, et al. (2012) Identification and characterization of the first SLC11A2 isoform 1a mutation causing a defect in splicing process and an hypomorphic allele expression of the SLC11A2 gene. Br J Haematol 159: 492–495. doi: 10.1111/bjh.12062
![]() |
[68] |
Bardou-Jacquet E, Island ML, Jouanolle AM, et al. (2011) A novel N491S mutation in the human SLC11A2 gene impairs protein trafficking and in association with the G212V mutation leads to microcytic anemia and liver iron overload. Blood Cells Mol Dis 47: 243–248. doi: 10.1016/j.bcmd.2011.07.004
![]() |
[69] |
Blanco E, Kannengiesser C, Grandchamp B, et al. (2009) Not all DMT1 mutations lead to iron overload. Blood Cells Mol Dis 43: 199–201. doi: 10.1016/j.bcmd.2009.05.003
![]() |
[70] |
Iolascon A, De FL (2009) Mutations in the gene encoding DMT1: clinical presentation and treatment. Semin Hematol 46: 358–370. doi: 10.1053/j.seminhematol.2009.06.005
![]() |
[71] | Iolascon A, d'Apolito M, Servedio V, et al. (2006) Microcytic anemia and hepatic iron overload in a child with compound heterozygous mutations in DMT1 (SCL11A2) Blood 107: 349–354. |
[72] |
Iolascon A, Camaschella C, Pospisilova D, et al. (2008) Natural history of recessive inheritance of DMT1 mutations. J Pediatr 152: 136–139. doi: 10.1016/j.jpeds.2007.08.041
![]() |
[73] | Mims MP, Guan Y, Pospisilova D, et al. (2005) Identification of a human mutation of DMT1 in a patient with microcytic anemia and iron overload. Blood 105: 1337–1342. |
[74] |
Simmons KJ, Jackson SM, Brueckner F, et al. (2014) Molecular mechanism of ligand recognition by membrane transport protein, Mhp1. EMBO J 33: 1831–1844. doi: 10.15252/embj.201387557
![]() |
[75] | Alva V, Soding J, Lupas AN (2015) A vocabulary of ancient peptides at the origin of folded proteins. Elife 4. |
[76] |
Das S, Dawson NL, Orengo CA (2015) Diversity in protein domain superfamilies. Curr Opin Genet Dev 35: 40–49. doi: 10.1016/j.gde.2015.09.005
![]() |
[77] | Vergara-Jaque A, Fenollar-Ferrer C, Kaufmann D, et al. (2015) Repeat-swap homology modeling of secondary active transporters: updated protocol and prediction of elevator-type mechanisms. Front Pharmacol 6: 183. |
[78] |
Siddle KJ, Quintana-Murci L (2014) The Red Queen's long race: human adaptation to pathogen pressure. Curr Opin Genet Dev 29: 31–38. doi: 10.1016/j.gde.2014.07.004
![]() |
[79] |
Nakashige TG, Zhang B, Krebs C, et al. (2015) Human calprotectin is an iron-sequestering host-defense protein. Nat Chem Biol 11: 765–771. doi: 10.1038/nchembio.1891
![]() |
[80] |
Becker KW, Skaar EP (2014) Metal limitation and toxicity at the interface between host and pathogen. FEMS Microbiol Rev 38: 1235–1249. doi: 10.1111/1574-6976.12087
![]() |
[81] | Lisher JP, Giedroc DP (2013) Manganese acquisition and homeostasis at the host-pathogen interface. Front Cell Infect Microbiol 3: 91. |
[82] |
Ganz T (2009) Iron in innate immunity: starve the invaders. Curr Opin Immunol 21: 63–67. doi: 10.1016/j.coi.2009.01.011
![]() |
[83] |
Ganz T, Nemeth E (2015) Iron homeostasis in host defence and inflammation. Nat Rev Immunol 15: 500–510. doi: 10.1038/nri3863
![]() |
[84] |
Buracco S, Peracino B, Cinquetti R, et al. (2015) Dictyostelium Nramp1, which is structurally and functionally similar to mammalian DMT1 transporter, mediates phagosomal iron efflux. J Cell Sci 128: 3304–3316. doi: 10.1242/jcs.173153
![]() |
[85] |
Gallant CJ, Malik S, Jabado N, et al. (2007) Reduced in vitro functional activity of human NRAMP1 (SLC11A1) allele that predisposes to increased risk of pediatric tuberculosis disease. Genes Immun 8: 691–698. doi: 10.1038/sj.gene.6364435
![]() |
[86] |
Desiro A, Salvioli A, Ngonkeu EL, et al. (2014) Detection of a novel intracellular microbiome hosted in arbuscular mycorrhizal fungi. ISME J 8: 257–270. doi: 10.1038/ismej.2013.151
![]() |
[87] | Ohshima S, Sato Y, Fujimura R, et al. (2016) Mycoavidus cysteinexigens gen. nov., sp. nov., an endohyphal bacterium isolated from a soil isolate of the fungus Mortierella elongata. Int J Syst Evol Microbiol. |
[88] |
Cohen A, Nevo Y, Nelson N (2003) The first external loop of the metal ion transporter DCT1 is involved in metal ion binding and specificity. Proc Natl Acad Sci USA 100: 10694–10699. doi: 10.1073/pnas.1934572100
![]() |
[89] |
Courville P, Chaloupka R, Cellier MF (2006) Recent progress in structure-function analyses of Nramp proton-dependent metal-ion transporters. Biochem Cell Biol 84: 960–978. doi: 10.1139/o06-193
![]() |
[90] |
Chen X, Peng J, Cohen A, et al. (1999) Yeast SMF1 mediates H+ -coupled iron uptake with concomitant uncoupled cation currents. J Biol Chem 274: 35089–35094. doi: 10.1074/jbc.274.49.35089
![]() |
[91] |
Agranoff D, Collins L, Kehres D, et al. (2005) The Nramp orthologue of Cryptococcus neoformans is a pH-dependent transporter of manganese, iron, cobalt and nickel. Biochem J 385: 225–232. doi: 10.1042/BJ20040836
![]() |
[92] |
Gunshin H, Mackenzie B, Berger UV, et al. (1997) Cloning and characterization of a mammalian proton-coupled metal-ion transporter. Nature 388: 482–488. doi: 10.1038/41343
![]() |
[93] | Sacher A, Cohen A, Nelson N (2001) Properties of the mammalian and yeast metal-ion transporters DCT1 and Smf1p expressed in Xenopus laevis oocytes. J Exp Biol 204: 1053–1061. |
[94] |
Bleackley MR, MacGillivray RT (2011) Transition metal homeostasis: from yeast to human disease. Biometals 24: 785–809. doi: 10.1007/s10534-011-9451-4
![]() |
[95] |
Martin JE, Waters LS, Storz G, et al. (2015) The Escherichia coli small protein MntS and exporter MntP optimize the intracellular concentration of manganese. PLoS Genet 11: e1004977. doi: 10.1371/journal.pgen.1004977
![]() |
[96] |
Tsai MF, Fang Y, Miller C (2012) Sided functions of an arginine-agmatine antiporter oriented in liposomes. Biochemistry 51: 1577–1585. doi: 10.1021/bi201897t
![]() |
[97] |
Nevo Y, Nelson N (2006) The NRAMP family of metal-ion transporters. Biochim Biophys Acta 1763: 609–620. doi: 10.1016/j.bbamcr.2006.05.007
![]() |
[98] |
Lan WJ, Ren HL, Pang Y, et al. (2012) A facile transport assay for H+ coupled membrane transport using fluorescence probes. Analytical Methods 4: 44–46. doi: 10.1039/C1AY05549F
![]() |
[99] |
Makui H, Roig E, Cole ST, et al. (2000) Identification of the Escherichia coli K-12 Nramp orthologue (MntH) as a selective divalent metal ion transporter. Mol Microbiol 35: 1065–1078. doi: 10.1046/j.1365-2958.2000.01774.x
![]() |
[100] |
Kehres DG, Zaharik ML, Finlay BB, et al. (2000) The NRAMP proteins of Salmonella typhimurium and Escherichia coli are selective manganese transporters involved in the response to reactive oxygen. Mol Microbiol 36: 1085–1100. doi: 10.1046/j.1365-2958.2000.01922.x
![]() |
[101] |
Perry RD, Mier I, Fetherston JD (2007) Roles of the Yfe and Feo transporters of Yersinia pestis in iron uptake and intracellular growth. Biometals 20: 699–703. doi: 10.1007/s10534-006-9051-x
![]() |
[102] |
Hohle TH, O'Brian MR (2009) The mntH gene encodes the major Mn(2+) transporter in Bradyrhizobium japonicum and is regulated by manganese via the Fur protein. Mol Microbiol 72: 399–409. doi: 10.1111/j.1365-2958.2009.06650.x
![]() |
[103] | Fleming MD, Trenor CC, Su MA, et al. (1997) Microcytic anaemia mice have a mutation in Nramp2, a candidate iron transporter gene. Nat Genet 16: 383–386. |
[104] |
Nevo Y, Nelson N (2004) The mutation F227I increases the coupling of metal ion transport in DCT1. J Biol Chem 279: 53056–53061. doi: 10.1074/jbc.M408398200
![]() |
[105] |
Penmatsa A, Gouaux E (2014) How LeuT shapes our understanding of the mechanisms of sodium-coupled neurotransmitter transporters. J Physiol 592: 863–869. doi: 10.1113/jphysiol.2013.259051
![]() |
[106] |
Nevo Y (2008) Site-directed mutagenesis investigation of coupling properties of metal ion transport by DCT1. Biochim Biophys Acta 1778: 334–341. doi: 10.1016/j.bbamem.2007.10.007
![]() |
[107] |
Ivankov DN, Finkelstein AV, Kondrashov FA (2014) A structural perspective of compensatory evolution. Curr Opin Struct Biol 26: 104–112. doi: 10.1016/j.sbi.2014.05.004
![]() |
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Parameters | Units | Minimum | Maximum | Mean | Standard Deviation | Standard Value [14] |
pH | - | 6.72 | 7.60 | 7.17 | 0.32 | 6–9 |
EC | S/cm | 0.0015 | 0.0085 | 0.0036 | 0.0028 | 0.0012 |
Hardness as CaCO3 | mg/L | 422 | 745 | 585.20 | 126.08 | N/A |
TS | mg/L | 218.00 | 327.20 | 256.16 | 42.15 | N/A |
TSS | mg/L | 184 | 258 | 212 | 27.68 | 150–500 |
TDS | mg/L | 72 | 173 | 110.40 | 39.09 | 2100 |
DO | mg/L | 0.40 | 2.83 | 1.05 | 1.01 | 4.5–8 |
BOD5d (at 20 ℃) | mg/L | 16 | 71 | 48.60 | 21.87 | 50 (inland surface)–250 (irrigated land) |
COD | mg/L | 69 | 269 | 175.4 | 74.59 | 200 (inland surface)–400 (irrigated land) |
Study Area | pH | EC (s/cm) | Hardness as CaCO3 (mg/L) | TS (mg/L) | TSS (mg/L) | TDS (mg/L) | DO (mg/L) | BOD (mg/L) | COD (mg/L) | Reference |
Jhenaidah (n = 70) | 7.17 (6.72–7.60) | 0.0036 (0.0015–0.0085) | 585.20 (422–745) | 256.16 (218–327.20) | 212 (184–258) | 110.40 (72–173) | 1.05 (0.40–2.83) | 48.60 (16–71) | 175.40 (69–269) | This Study |
Sylhet City (n = 6) | 6.24–6.89 | - | - | 450 (300–600) | 150 (100–200) | 300 (200–400) | 40 (0–80) | 138 (108–168) | - | [15] |
Narayanganj (n = 9) | 9.88 | 0.014 (0.001–0.06) | - | - | 1123.11 (736–1960) | 9123.78 (791–46700) | 2.36 (0.42–4.6) | 573.89 (415–770) | 1223.33 (860–1560) | [9] |
Hazaribag, Dhaka (n = 10) | 7.19 | 0.006 | - | - | 1613 | 3455 | 1.74 | 548.50 | 1441 | [8] |
Hazaribag, Dhaka (n = 12) | 6.56 | 0.005 | - | - | 2048.42 | 3516.50 | 1.58 | 754.50 | 2776.83 | [7] |
Different areas of Bangladesh | 3.9–14 | 0.00025–0.064 | - | - | 24.9–39.50 | 90.7–5980 | 0–7 | 10–786 | 41–2430 | [10] |
Kushtia (n = 8) | 4.19 –8.49 | 0.00084–0.003 | 485–848 | - | - | - | - | 57–88 | 150–108 | [6] |
Chittagong (n = 10) | 8.23 (6.80–10.21) | 0.004 (0.0005–0.025) | 78.02 (9.20 –192) | - | 186.50 (16–480) | 2577.90 (233–12570) | 1.72 (0.1–3.6) | 155.59 (12.40–276.90) | 507.54 (145.47 –1854.60) | [5] |
Parameters | Units | Minimum | Maximum | Mean | Standard Deviation | Standard Value [14] |
pH | - | 6.72 | 7.60 | 7.17 | 0.32 | 6–9 |
EC | S/cm | 0.0015 | 0.0085 | 0.0036 | 0.0028 | 0.0012 |
Hardness as CaCO3 | mg/L | 422 | 745 | 585.20 | 126.08 | N/A |
TS | mg/L | 218.00 | 327.20 | 256.16 | 42.15 | N/A |
TSS | mg/L | 184 | 258 | 212 | 27.68 | 150–500 |
TDS | mg/L | 72 | 173 | 110.40 | 39.09 | 2100 |
DO | mg/L | 0.40 | 2.83 | 1.05 | 1.01 | 4.5–8 |
BOD5d (at 20 ℃) | mg/L | 16 | 71 | 48.60 | 21.87 | 50 (inland surface)–250 (irrigated land) |
COD | mg/L | 69 | 269 | 175.4 | 74.59 | 200 (inland surface)–400 (irrigated land) |
Study Area | pH | EC (s/cm) | Hardness as CaCO3 (mg/L) | TS (mg/L) | TSS (mg/L) | TDS (mg/L) | DO (mg/L) | BOD (mg/L) | COD (mg/L) | Reference |
Jhenaidah (n = 70) | 7.17 (6.72–7.60) | 0.0036 (0.0015–0.0085) | 585.20 (422–745) | 256.16 (218–327.20) | 212 (184–258) | 110.40 (72–173) | 1.05 (0.40–2.83) | 48.60 (16–71) | 175.40 (69–269) | This Study |
Sylhet City (n = 6) | 6.24–6.89 | - | - | 450 (300–600) | 150 (100–200) | 300 (200–400) | 40 (0–80) | 138 (108–168) | - | [15] |
Narayanganj (n = 9) | 9.88 | 0.014 (0.001–0.06) | - | - | 1123.11 (736–1960) | 9123.78 (791–46700) | 2.36 (0.42–4.6) | 573.89 (415–770) | 1223.33 (860–1560) | [9] |
Hazaribag, Dhaka (n = 10) | 7.19 | 0.006 | - | - | 1613 | 3455 | 1.74 | 548.50 | 1441 | [8] |
Hazaribag, Dhaka (n = 12) | 6.56 | 0.005 | - | - | 2048.42 | 3516.50 | 1.58 | 754.50 | 2776.83 | [7] |
Different areas of Bangladesh | 3.9–14 | 0.00025–0.064 | - | - | 24.9–39.50 | 90.7–5980 | 0–7 | 10–786 | 41–2430 | [10] |
Kushtia (n = 8) | 4.19 –8.49 | 0.00084–0.003 | 485–848 | - | - | - | - | 57–88 | 150–108 | [6] |
Chittagong (n = 10) | 8.23 (6.80–10.21) | 0.004 (0.0005–0.025) | 78.02 (9.20 –192) | - | 186.50 (16–480) | 2577.90 (233–12570) | 1.72 (0.1–3.6) | 155.59 (12.40–276.90) | 507.54 (145.47 –1854.60) | [5] |