Heavy Metal Pollution in Street Dust: A Comprehensive Study on Risk Assessment and Source Identification in a Highly Industrialized Area of Bangladesh

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Hasibur Rahaman, Md. Alinur Rahman, Rahamoni Khanam, Minhaz Ahmed, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3768053/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The study is intended to report the level of heavy metals (HMs) contamination, their potential source, and their impacts by analyzing street dust (SD) samples collected from thirty distinct sampling locations in Narayanganj Sadar Upazila, Bangladesh. The results suggest that the average concentrations of Chromium (Cr), Copper (Cu), Nickel (Ni), Cadmium (Cd), Lead (Pb), Manganese (Mn), Sodium (Na), Calcium (Ca), and Magnesium (Mg) were 317.25 ± 62.25, 247.86 ± 25.76, 53.26 ± 16.76, 3.53 ± 2.03, 56.35 ± 31.76, 443.94 ± 6.48, 227.18 ± 33.86, 101.74 ± 3.79, 4842 ± 203.90, and 79.46 ± 1.70 mg kg − 1 , respectively. Both Cr and Cu levels were over five and ten times higher than the background values, respectively. Principal component analysis (PCA) and positive matrix factorization (PMF) suggest that industrial activities and heavy traffic on the street could be the potential sources. Moreover, Cr, Cu, and Cd all exhibit 'very high’ contamination factors (CF), with corresponding enrichment factors (EF) categorized as 'significant', 'very high’, and 'high', respectively. The geo-accumulation index (I geo ) found a moderately to strongly polluted category for Cu and a strong to extremely polluted category for Cd. Risk indices indicate that potential carcinogenic and non-carcinogenic risks were notably higher for children compared to adults, with the primary mode of exposure being ingestion. Street dust heavy metals health risk environmental risk Bangladesh Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Rapid urbanization produces a vast amount of trash daily due to the increased population (Faisal et al., 2021 ). The burning and degradation of these waste materials and emissions from vehicles and manufacturing industries may accumulate heavy metals (HMs) in the environment (Xu et al., 2010 ). The fine and ultra-fine particles, aerosols, and suspended chemicals end up in the air and seriously damage the air quality; after a while, these toxic elements settle down on the surface with precipitation and damage the soil quality (Faisal et al., 2021 ). The anthropogenic sources of HM contamination can mainly be categorized as industrial, agricultural, and sewage pollution (CHEN Shibao, 2019 ; El-Zeiny & Abd El-Hamid, 2022 ). Nevertheless, previous studies reported that vehicular emissions are primarily responsible for emitting harmful toxins, potentially dangerous elements, and HMs from the combustion of fossil fuel and consumable materials (e.g., chemicals, lubricants, rubber, and brake pads) (Prokof’eva et al., 2017 ). Moreover, studies reported different anthropogenic sources and factors for HMs in street dust (SD). For instance, Pb, Zn, Cu, and Cr from the atmospheric settlement; Ni from various natural sources; and Cd deposited from the industrial effluents (Kabir et al., 2021 ; Safiur Rahman et al., 2019 ; Shi et al., 2008 ). Pb deposition in roadside dust and soil is caused by the use of Pb-induced gasoline in prior decades. Cu, Zn, and Cd are emitted due to different types of abrasion, motor oil, and industrial pollution. The corrosion of motor vehicle parts' painted layers and chrome plating is thought to be the source of Ni, Cd, and Cr in SD (Gupta & Arya, 2016 ). Since they have an extended persistence capacity, those elements settled down and recirculated through different physicochemical processes in the ecosystem (ISLAM et al., 2020 ; Kabir et al., 2021 ). Moreover, the accumulation of toxicants within SD serves as both sink and source of HM pollution, which makes SD an indicator for determining the HMs pollution level in the urban area (J. Huang et al., 2016 ; Kabir et al., 2021 ; Urrutia-Goyes et al., 2018 ). The concern arises from the issue that it is challenging to control the impairment of soil environmental quality by HMs which pose a great possible threat to the human health and well-beings (Burges et al., 2015 ; El-Zeiny & Abd El-Hamid, 2022 ; Zhang et al., 2018 ). The HMs can enter the environmental systems through different natural pathways (e.g., weathering, erosion, lithogenesis) and enter into the human body through different anthropogenic activities (e.g., smelting)(Adnan et al., 2022 ; Masindi & Muedi, 2018 ; Stafilov et al., 2010 ; W. Wang et al., 2020 ). HMs can damage the liver and respiratory functions, the cardiovascular and hematopoietic systems, and the gastrointestinal and endocrine systems (Faisal et al., 2021 ; Safiur Rahman et al., 2019 ). The minute particles, like aerosols and street dust, possess the alarming ability to swiftly infiltrate the human body through ingestion, inhalation, or dermal contact, exacting a profound toll on human health, with children being especially vulnerable, while simultaneously wreaking havoc on the delicate balance of our environment (Kabir et al., 2021 ; Sezgin et al., 2004 ; Shi et al., 2008 , 2011 ). Since the intrusion of HMs into the human body from surrounding exposure cannot be easily minimized, preventing the emission from sources is an efficient control measure. Henceforth, identifying the emission source, migration path, and potential HMs risk to public health and the ecosystem is essential (Sun et al., 2022 ). Previously, receptor models, such as principal component analysis, cluster analysis, and factor analysis, were applied for source identification. However, these models fail to access the non-negative results below the detection limit (Y. Li et al., 2018 ; Paatero & Tapper, 1994 ; Sun et al., 2022 ). To overcome the limitations, the positive matrix factorization (PMF) model was used (Fei et al., 2020 ; Guan et al., 2018 ; Paatero, 1997 ). In addition, the application of geospatial techniques along with remote sensing is efficient for representing the distribution of contaminants levels in the spatial map of the locations, which supports identifying the sources and monitoring the contribution of each component (El-Zeiny et al., 2019 ; El-Zeiny & Abd El-Hamid, 2022 ). Effects of HMs polluted street dust on the health of city dwellers were well reported in many developed and rapidly urbanized countries, such as China, Spain, Poland, Greece, and Hong Kong (Delgado-Iniesta et al., 2022 ; Wei et al., 2015 ; Zgłobicki & Telecka, 2021 ). In Bangladesh, some studies were also conducted in the Dhaka, Kustia-Jhenaidah region, and Gazipur cities that reported the human and ecological risks from the HMs in SD (Kabir et al., 2021 ; Kormoker, Kabir, et al., 2022 ). Applying multivariate analyses (Kabir et al., 2022 ) reported that industrial and vehicular emissions are largely responsible for HMs deposition in SD, by conducting a study in the Kustia-Jhenaidah highway, Bangladesh. (Kabir et al., 2021 ) ) reported significant ecological risks; however, they found insignificant carcinogenic and non-carcinogenic risks for humans. Additionally, their study indicated that children are more vulnerable than adults. In other study (Kormoker, Kabir, et al., 2022 ) reported the potential noncarcinogenic risks for adults and Childs. However, there is a significant research gap about the HMs contamination in terms of SD in Bangladesh and their source identifications. Narayanganj, the central part of Bangladesh, comprises many world-class jute mills and textile industries, making it a regional industrial center (Noman et al., 2016 ). Moreover, country's largest river port belongs to the Narayanganj city area, popularly known as the "Dundee of East." Consequently, in many manufacturing industries, on highways, and in planned and unplanned settlements, immense crowding is common nowadays. For facilitating the transportation of people and goods, the rapid growth in the number of vehicles has congested the roads and highways (Seddique et al., 2014 ). The engine fuel-burning emissions from the metal processing industries may impair the air quality and thoughtfully contribute to HM pollution. However, there has yet to be conducted a study on Narayanganj City to address the ecological, environmental and human health risks from HMs pollution. Therefore, it is crucial to characterize the HMs from the SD in different point locations (e.g., industrial, residential, bus stoppage, and market area) and measure the potential ecological and human health risks. Since no study mentioned the issues in the Narayanganj Sadar area, the current study aimed to determine the spatial distribution of HMs using geospatial analysis, possible source identification using multivariate analyses, and the ecological and human health risks by applying numerous health indices. That could serve as reference data for further research. Moreover, it could help in national policy-making and the implementation of development projects. 2. Method and materials 2.1. Study Area Narayanganj is a former Dhaka sub-divisional town, and Bangladesh's oldest and most famous river port has evolved into a trade and commerce center. That is, it is bounded on the north by the districts of Gazipur and Narsingdi, on the east by the districts of Brahmanbaria and Comilla, on the south by the district of Munshiganj, and on the west by the district of Dhaka. The district is between 23'33" and 23'57" north latitude and 90'26" to 90'45" east longitude. It covers a land area of 760 km 2 . The yearly temperature varies between 17.6 and 29.8˚C, and the annual rainfall was recorded at 376 mm in 2011. Some non-tidal rivers (such as the Meghna, Dhaleshwari, Buriganga, and Balu) cross the district, facilitating water supply, drainage, and irrigation (BBS, 2013 ). The study was conducted at thirty specific sampling points (more specifications of the sampling points can be found in Table S1 ) of the street in the Narayanganj Sadar Upazila, which has 1.5 million inhabitants and lies about 20 kilometers southeast of Dhaka city. Rapid growth in the garment industries, global trading (import and export), brickfields, knitwear garments, shipyards, and other commercial activities are annihilating the environmental condition in that region (Md. A. Rahman et al., 2022 ). The study collected dust samples from different roadside points, as pointed out in Fig. 1 . Sampling area, and sampling locations of the study 2.2. Sample collection The sampling stations were chosen based on the importance of the sites, such as population density, traffic loads, surrounding land use patterns, presumed dust quality, and pollution extent. Since less rainfall occurs during the dry season, February was selected for the entire sample collection period (Kabir et al., 2021 ). In the Narayanganj Sadar Upazila, a total of 30 cumulative roadside dust samples were collected from various sampling points. With the help of a clean broom and a plastic packer, 500 g of dust samples were gathered from each location. We collected four dust samples at a distance of 0–10 m from each side of the road to make a composite sample. All the information about the sample location is given in Table S1 . 2.3. Sample Preparation and Analysis In the laboratory, all samples were air-dried for a week (Safiur Rahman et al., 2019 ). The samples were further dried in an oven for 24 hours at 85°C. A stainless steel sieve with a mesh size of 75µm was used to sift the samples for small particles. This particular size was chosen for study because it can readily accumulate a high concentration of heavy metals, be easily resuspended, and be inhaled through the nose or mouth during breathing (Bisht et al., 2022). Nitric acid (HNO 3 ) and perchloric acid (HClO 4 ) were used to acid digest the 1 g dust sample. The Inductive Coupled Plasma (Agilent- 5800 ICP-OES, USA), and Adsorption Atomic Spectroscopy instrument (AAS Instrument Model: PinAAcle 900H, Perkin Elmer, Waltham, Massachusetts, USA) were used to assess the presence of metals (Taiwo et al., 2020 ) such as Cr, Cu, Ni, Cd, Pb, Fe, Mn, Na, Ca, and Mg. 2.4. Quality assurance Using reagent blanks and sample replication, an analytical approach was used to measure precision and bias. The research revealed that the bias and accuracy were both under 10%. Plastic and glassware were marinated in 2% HNO 3 for 24 hours before being cleaned with detergent, rinsed entirely with tap and distilled water, then oven-dried at 37°C before use. Accuracy in the ICP-OES and AAS calibration curves were maintained through the careful selection of standards and instrument optimization. 2.5. Statistical Analysis The descriptive statistical analyses were performed using R Studio desktop V-1.3.1093. We got the assumption about the normality of the dataset using the Kolmogorov-Smirnov (K-S) test. After that, we conducted a one-way analysis of variance (ANOVA) to compare the variation among the sampling locations for each parameter. Pearson parametric correlation was applied to assess the relationship among the HMs in different sampling locations. Multivariate analyses such as principal component analysis (PCA), cluster analysis (CA), and dendrograms were performed using suitable packages in R Studio. Moreover, the PMF model was used to determine several factors performed by the EPA PMF-5.0 software. The spatial distributions of investigated heavy metals such as Cr, Cu, Ni, Cd, Pb, Fe, Mn, Na, Ca, and Mg in Narayanganj Sadar road dust samples are obtained using ArcGIS software (Version 10.5, USA). Critical ecological, environmental, and human health risk assessment indices were estimated using mathematical formulas as delineated in Table 1 and according to the previous studies. Table 1 Pollution risk indices for ecological, environmental and human health risk assessment Environmental and Ecological risk assessment Category References The enrichment factor (EF) : \(\text{E}\text{F}=\frac{\left( \frac{{\text{C}}_{\text{n}}}{{\text{C}}_{\text{r}\text{e}\text{f}}}\right)\text{s}\text{a}\text{m}\text{p}\text{l}\text{e}}{\left(\frac{{\text{B}}_{\text{n}}}{{\text{B}}_{\text{r}\text{e}\text{f}}}\right)\text{b}\text{a}\text{c}\text{k}\text{g}\text{r}\text{o}\text{u}\text{n}\text{d}}\) ………….…. (1.1) C n = the concentration of the examined element in the examined environment, C ref = the concentration of the examined element in the reference environment, B n (background) = the concentration of the reference element in the examined environment, B ref (background) = the concentration of the reference element in the reference environment EF < 2: minimal enrichment 2 ≤ EF < 5: moderate enrichment 5 ≤ EF < 20: significant enrichment 20 ≤ EF < 40: very high enrichment EF ≥ 40: extremely high enrichment. [38] [39] [8] [40] [41] [42] ; [43]. Geo-accumulation Index (l geo ) \({\mathbf{I}}_{\mathbf{g}\mathbf{e}\mathbf{o} = {\mathbf{log}}_{2}({\mathbf{C}}_{\mathbf{n}/1.5{\mathbf{B}}_{\mathbf{n} )}}}\) ………………. (1.2) C n = The measured concentration of metal (n) in the soil B n = The background value of element (n) in the background sample Igeo ≤ 0: practically unpolluted 0 ≤ Igeo ≤ 1: unpolluted to moderately polluted 1 ≤ Igeo ≤ 2: moderately polluted 2 ≤ Igeo ≤ 3: moderately to strongly polluted 3 ≤ Igeo ≤ 4: strongly polluted 4 ≤ Igeo ≤ 5: strongly to extremely polluted Igeo > 5: extremely polluted [38] [44] [39] Contamination Factor (CF) \(\varvec{C}\varvec{F} = \frac{{\varvec{C}}_{\varvec{m}\varvec{e}\varvec{t}\varvec{a}\varvec{l}}}{{\varvec{C}}_{\varvec{b}\varvec{a}\varvec{c}\varvec{k}\varvec{g}\varvec{r}\varvec{o}\varvec{u}\varvec{n}\varvec{d}}}\) . …………. (1.3) C metal = Concentration of heavy metals C background = Concentration of background values of heavy metals CF < 1: Low degree 1 ≤ CF < 3: Moderate degree 3 ≤ CF < 6: Considerable degree CF ≥ 6: Very high degree [38] [44] Human health risk methodology The average daily dose (ADD) of each heavy metal (mg kg − 1 day − 1 ) : \(\text{A}\text{D}\text{D}\left(\text{i}\text{n}\text{g}\right)=\frac{ \text{C} \times \text{I}\text{n}\text{g}\text{R} \times \text{E}\text{F} \times \text{E}\text{D} }{\text{B}\text{W} \times \text{A}\text{T}}\times {10}^{-6}\) ... ………………………. (1.4) \(\text{A}\text{D}\text{D}\left(\text{i}\text{n}\text{h}\right) = \frac{\text{C} \times \text{I}\text{n}\text{h}\text{R} \times \text{E}\text{F} \times \text{E}\text{D}}{\text{P}\text{E}\text{F} \times \text{B}\text{W} \times \text{A}\text{T}}\) ……. (1.5) \(\text{A}\text{D}\text{D}\left(\text{d}\text{e}\text{r}\text{m}\text{a}\text{l}\right) = \frac{\text{C}\times \text{S}\text{A} \times \text{A}\text{F} \times \text{A}\text{B}\text{F}\times \text{E}\text{F}\times \text{E}\text{D}}{\text{B}\text{W}\times \text{A}\text{T}}\times {10}^{-6}\) ………………………………. (1.6) Parameter Value (Child) Value (Adult) IngR = Ingestion rate (mg/day) 200 100 InhR = Inhalation rate (m 3 /day) 7.63 12.8 PEF = Particle Emission Factor 1.36E + 09 1.36E + 09 SA = Surface of exposed skin area (cm 2 ) 2800 5700 ABF = Dermal Absorption Factor 0.001 0.001 AF = Skin Adherence Factor(mg/cm 2 ) 0.2 0.07 ED = Duration of Exposure (years) 6 24 EF = Frequency of Exposure (days/year) 350 350 AT = Average Time Non- Carcinogens(days) ED*365 ED*365 70*365 70*365 BW = Body Weight (kg) 15 70 [45] [41] [40] [46] Hazard index (HI) Reference and slope factor of metals \(\text{H}\text{I}=\left(\text{H}\text{Q}\right)\text{i}\text{n}\text{g}+\left(\text{H}\text{Q}\right)\text{i}\text{n}\text{h}+\left(\text{H}\text{Q}\right)\text{d}\text{e}\text{r}\text{m}\text{a}\text{l}\) ………………….………. (1.7) \(\text{H}\text{I}=\left(\frac{\text{A}\text{D}\text{D}}{\text{R}\text{f}\text{D}}\right)\text{i}\text{n}\text{g}\text{e}\text{s}\text{t}\text{i}\text{o}\text{n}+\left(\frac{\text{A}\text{D}\text{D}}{\text{R}\text{f}\text{D}}\right)\text{i}\text{n}\text{h}\text{a}\text{l}\text{a}\text{t}\text{i}\text{o}\text{n}+\left(\frac{\text{A}\text{D}\text{D}}{\text{R}\text{f}\text{D}}\right)\text{d}\text{e}\text{r}\text{m}\text{a}\text{l}\) …………..……………… (1.8) The HQ is the ratio of a metal’s ADD to its reference dose (RfD) for exposure pathways that are identical Metal RfD ing RfD inh RfD dermal SF inh Cr 3.00E-03 2.86E-05 6.00E-05 4.20E + 01 Cu 4.00E-02 4.02E-02 1.20E-02 - Ni 2.00E-02 2.06E-02 5.40E-03 8.40E-01 Cd 1.00E-03 1.00E-03 1.00E-05 6.30E + 00 Pb 3.50E-03 3.52E-03 5.25E-04 - Mn 4.60E-02 1.43E-05 1.84E-03 - [21,47] Carcinogenic Risk (CR) Assessment \(CR=ADD \times SF\) ……………………….……. (1.9) \(TCR= \sum CR\) ……………….………… (2.0) ADD = The average daily dose of each heavy metal (mg kg − 1 day − 1 ) and SF = is the slope factor (mg kg − 1 day − 1 ) −1 . TCR = Total Carcinogenic Risk Positive Matrix Factorization (PMF) : X ij \(=+{\sum }_{\text{k}=1}^{\text{p}}{\text{g}}_{\text{i}\text{k}}{\text{f}}_{\text{k}\text{f}}+ {\text{e}}_{\text{i}\text{j}}\) Q = \(\sum _{\text{i}=1 }^{\text{n}}\sum _{\text{j}=1}^{\text{m}} \left\{\frac{{\text{e}}_{\text{i}\text{j}}}{{\text{u}}_{\text{i}\text{j}}}\right\}\) 2 X ij = Species j concentration on sample i p = Number of factors g ik = Relevant factor contribution of k to i sample, f kf = Species’ concentration of j in profile factor k, and e ij is the residuals If X ij ≤ MDL, \({\text{U}}_{\text{i}\text{j}}=\frac{5}{6}\times \text{M}\text{D}\text{L}\) Uij = The uncertainty If X ij ≥ MDL, \(\text{U}\text{i}\text{j} = \sqrt[]{({{\sigma }\text{j} \times {\left.\left(\text{X}\text{i}\text{j}\right.\right)}^{2} + \left(\text{M}\text{D}\text{L}\right)2}_{ }}\) σj = The relative standard deviation MDL = Minimum detection limit (MDL) values (mg/kg): Cr: 2 Ni: 1 Cu:0.6 Zn: 1 As: 0.4 Cd: 0.09 Pb: 2 Hg: 0.002 [48] [49] [21] 3. Results and discussion 3.1. Concentration of the contaminants in the street dust samples The descriptive statistics of each element and HMs (Cr, Cu, Cd, Mn, Zn, Fe, Ni, Mg, Ca, and Na) are represented in the following Table 2 and Fig. 2 . The average concentrations of Cr, Cu, Ni, Cd, Pb, Mn, Na, Ca, and Mg were 317.25 ± 62.25, 247.86 ± 25.76, 53.26 ± 16.76, 3.53 ± 2.03, 56.35 ± 31.76, 443.94 ± 6.48, 227.18 ± 33.86, 101.74 ± 3.79, 4842 203.90, and 79.46 ± 1.70 mgkg − 1, respectively. The ten elements were ranged between Cr: 112.46-491.06, Cu: 138.13-261.25, Ni: 14.28–90.25, Cd: 1.78–11.75, Pb: 10.53-177.23, Fe: 419.5-462.5, Mn: 94.73-271.25, Na: 93.08-108.68, Ca: 4227.5-5002.5, and Mg: 75-82.68 mg/kg, respectively. The concentration of Cr and Cu are potentially higher than the study (Kabir et al., 2021 )that reported range (Cr: 55.2-122.8, Cu: 26.3–72.1); moreover, the concentration of Ni, Cd, and Pb are congruent to the detected values of the study. The descriptive statistics of each element are given in Table S2. Another study (Kabir et al., 2022 ) conducted in the Kustia district, Bangladesh, reported values in the range of Cr: 12.2–19.6, Cu: 58.6-116.2, Ni: 8.2–22.6, Cd: 0.08–0.28, and Pb: 32.5–78.9 mg/kg, respectively, from the street dust samples. However, these values are also significantly lower than the current study areas' concentration. In addition, all the values were compared with the background value, the maximum permissible contents of potentially toxic elements for agricultural soils (PTE-MPC), and the soil invertebrate toxicity reference values (SI-TRV). The average concentration of Cr, Cu, Ni, and Cd was considerably higher than the PTE-MPC stated reference value (Table S2), indicating that HM contamination poses a potential risk for agricultural production. Moreover, the concentration of Cr and Cu was beyond the SI-TRV, which indicates the risk potential for the soil-living invertebrates. Concentration of heavy metals from the sampling sites in Narayanganj Sadar Upazila, Bangladesh. A critical consideration from the study is that the Cr and Cu contamination levels are considerably higher than those reported (Kabir et al., 2021 , 2022 ; Kormoker, Kabir, et al., 2022 ) for the SD samples from the other region of Bangladesh. In urban environment, the sources of Cr in the SD could be combustion of fossil fuels, car corrosion, metal works, vehicle scrap workshops, painting and dying, and long-time traffic congestion on the road (Delibašić et al., 2020 ; Safiur Rahman et al., 2019 ; Tan et al., 2018 ). In comparing the global perspective, the Cr and Cu concentrations are significantly higher than the stated locations (Table 2 ) around the world that were reported by (Ambade, 2012 ; Y. Cai & Zhang, 2021 ; Duzgoren-Aydin et al., 2006 ; Jose & Srimuruganandam, 2020 ; Liang et al., 2019 ; Roy et al., 2022 ; Safiur Rahman et al., 2019 ; Sutherland & Tolosa, 2000 ), except the value mentioned by (Banerjee, 2003 ). In comparison to other regions within Bangladesh, the research found that the elevated levels of heavy metal contamination, thereby emphasizing the pressing environmental concerns within these specific areas (Islam et al., 2022 ; Kabir et al., 2021 , 2022 ; Kormoker, Idris, et al., 2022 ; Kormoker, Kabir, et al., 2022 ). Table 2 Comparison with global distribution of heavy metal concentrations (mg/kg) in street dust of Narayanganj Sadar Upazila, Bangladesh. Cities/Country Cr Cu Ni Cd Pb Fe Mn References Narayanganj Sadar, Bangladesh 317.25 247.86 53.26 3.53 56.35 443.94 227.18 This study Dhaka, Bangladesh - 46 26 - 74 - - (Ahmed & Ishiga, 2006 ) Dhaka, Bangladesh 124.7 98.9 - - 67.6 - - (Rakib et al., 2014 ) Dhaka, Bangladesh 144.3 49.8 37.1 11.6 18.9 - 261.5 (Safiur Rahman et al., 2019 ) Delhi, India 481.9 512.3 - 18.9 597.9 - - (Banerjee, 2003 ) Vellore, India 50.44 77.87 11.68 - 80.57 22,059.74 281.03 (Jose & Srimuruganandam, 2020 ) Bhopal, India 121.5 126.4 45.3 - 534.6 - 708.5 (Ambade, 2012 ) Calcutta, India 54 44 42 3.12 536 - - (Chatterjee & Banerjee, 1999 ) Nanjing, China 133 141 115 1.92 119 - 602 (Men et al., 2018 ) Beijing, China 69.33 72.13 26 0.64 201.8 - - (Du et al., 2013 ) Guangzhou, China 64.3 102 23.6 - 84.1 21,184 411 (Liang et al., 2019 ) Guangzhou, Southeast China 78.8 176 23 2.41 240 44,700 481 (Duzgoren-Aydin et al., 2006 ) Toronto, Canada 197.9 162 58.8 0.51 182.8 48,234.5 1407.2 (Men et al., 2018 ) Hawaii, USA 273 167 177 106 - - - (Sutherland & Tolosa, 2000 ) Palmero, Italy 218 98 14 1.1 544 - - (Varrica et al., 2003 ) BV-China* (CNEMC, 1990) 62.5 21.4 28.9 0.11 21.4 - 557 (Safiur Rahman et al., 2019 ) *BV-China CNEMC Background value by China Environmental Centre 3.2. Spatial Distribution of contaminants Depending on the contamination level, each element represented in the spatial map in Fig. 3 a, b, c, d, e, f, g, and h. According to the spatial distribution map of Mn, Fe, Ca, and Cr metals, sampling site N1 (a government school and college in Shampur) was the most polluted by HMs. Sampling sites N16, N5, and N8 are polluted sites for Cr, but relatively less than sampling site N2. Sampling locations N5, N8, and N26 are the textile industry, Kasimpur government primary school, and Shibu market, respectively. In the BISIC area (N7), several sorts of industrial units have sprouted up, are indiscriminately using resources, and have a high concentration of Cr, Ca, Cu, Na, and Mn. The industrial processing, textile and dying could potentially enhance the HMs in the environment (Begum & Huq, 2016 ). The combustion of lubricant oil dominates the Cr, Cu, Ni and Mn emission, according to the study by (Pulles et al., 2012 ). Moreover, the steel production and coal-fired industries might pose a serious risk for HMs contamination (Hu et al., 2018 ). In addition, the large proportion of Pb emission could occur due to the industrial activities around the roadside (Qu et al., 2018 ). In the sampling sites N18, N21, N23, and N10, moderate to high level of Mg was assessed. Sampling sites N10, N18, N21, and N23 were the premier cement industry, bazaar, petroleum filling station, and heavy traffic road area, respectively. All types of cement used to construct roads, bridges, and building components contain magnesium as a common component. The majority of Mg was found in the soils that surround roads and highways, particularly those that are located near parks, forests, and other potentially unpolluted regions (Bućko et al., 2010 ). Na has a rich concentration at N15, N17, N14, N13, N30, N24, N12, N20, and N24. These are the locations of the following: garments, shops, heavy traffic areas, cinema hall, river port, hospital, and commercial areas, mostly. Pb has a high concentration at N1, N2, N19, N23, N24, and N26. These areas are linked to the Dhaka-Chittagong Highway, Steel manufacturing industrial area, EPZ Narayanganj, Filling Station, and Shibu Market. The concentration of Na in this area was likewise substantially greater in a few of the study locations, including (N10, N15, and N17). Despite the fact that Pb has been banned as an ingredient in gasoline for many years, the quantity of Pb in urban road dust still reflects the considerable amount of previous Pb pollution and the extended half-life of Pb in soils. Some of the Pb in road dust may have come from particles generated by industrial processes (Rawat et al., 2009 ). Spatial distribution of contaminants from each sampling point of the study area 3.3. Interrelationship of the potential toxic elements in the street dusts in the different sampling locations Pearson's correlation coefficient was used to determine the degree of correlation between HM concentrations, as well as offer hints about HM sources and routes. Anthropogenic activities are among the most significant factors influencing the relative abundance of HMs (Mitra et al., 2022 ). The concentrations of all metals are positively linked at the most contaminated location. Figure 4 shows the Pearson's correlation coefficients for metals in 30 road dust samples from the Narayanganj Sadar Upazila. The highest correlations (p < 0.001, p < 0.01) were identified between elemental groupings (Cr-Ni), (Cr-Fe), (Cr- Mn), (Cr- Mg), (Ni-Cd), (Ni-Pb), (Ni- Fe), (Ni- Mn), (Ni-Mg), (Cd-Pb), (Fe-Mn), (Fe-Mg), (Mn-Mg) revealing a similar origin for these elements, most likely due to contributions from other elements. The element pair (Cr- Cd), (Cu- Ni), (Cd-Fe), (Cd-Mn), (Cd-Mg), (Pb-Mn) significantly correlated at the point of (p < 0.05) are shown in Fig. 4 . According to published studies (W. L. Li & Pauluhn, 2010 ), if the correlation coefficient between heavy metal components is positive, these factors may have a common root, mutual dependency, and behavior during transportation. Correlation between the parameters of the street dust sample, p values (< 0, 0.001, 0.01, 0.05, 0.1, 1) symbols (“***”, “**”, “*”, “.”, “”) The results of cluster analysis (CA) presented in the cluster dendrogram (Fig. S1 ) and 2D cluster (Fig. 5 ) indicate major clusters of the thirty sampling points of the study area. Depending on the similar pattern of pollution indices, industrial settings, traffic volume, and commercial setup, three major clusters were defined in the analysis. Figure 5 represents cluster 1, which includes most of the sampling locations: 2 to 9, 13 to 23, 25, 26, 28, and 29. Cluster 2 is composed of two locations: 12 and 29, and cluster 3 is composed of sampling locations 1, 2, 10, 11, 20, 24, 26, 27, and 30. Cluster 1 mainly consists of residential and commercial setups. Among them, locations 2 to 6 were in the residential area, 7 to 15 in the commercial area, and the remaining were mixed industrial, commercial, and residential setups. The two sampling sites of cluster 2 were located entirely in the commercial urban area, and the cluster 3 sampling area was among the residential, commercial, and industrial areas. 2D Cluster represents the three homogenized cluster depending on the similarities of the sampling areas contamination. The findings of PCA revealed that the analyzed heavy metals are well represented by three principal components (PCs), which can explain 73.95% of the total variance (Fig. S2, Table S4). Five elements (Cr, Fe, Mn, Mg, and Ni) dominated the first PC (PC1), which explained 46.9% of the total variation. However, each element depicted an inverse correlation among themselves in PC1. The heavy industrial manufacturing processes, machinery plants could largely responsible for the Cr, Mn and Ni contribution level in the PC1 (Yeung et al., 2003 ). In case of the PC2, Cu, Ni, Cd, Pb, and Na contributed positively (Fig. 6 ) and a significant contribution was observed for Cu (Table S4). The element is widely applied for the tire preparation during the vulcanizing process, about 0.4–4.3% Cu consisting the resulted tire thread (Lu et al., 2010 ). Cu is used as corrosive resistance in different mechanical parts, the corrosion and dissolution occurrence could be regarded as a potential source (Lu et al., 2010 ). Therefore, the heavy traffic density, and corrosion of tires in busy industrial and commercial areas could be the potential source of the contamination level. According to the importance of the components, PC3's (13.30%) variance showed a similar trend compared to PC2's (13.77%). In the PC3, Cr, Cu, Ni, Fe, Mn, and Mg contributed positively, while a significant negative contribution could be observed for Na. Numerous human induced pollution and atmospheric particle deposition could potentially contribute to different heavy metal addition in the urban settlements, more specifically, Ni, Cu, Cd, Pb, As and Zn could be added in the road dust due to different kinds of emissions (Iqbal & Shah, 2011 ; Islam et al., 2022 ; Lu et al., 2010 ; Manzoor et al., 2006 ; Pandey et al., 2012 ). Local anthropogenic activities are responsible for Pb, Cu, Ni and Cd contamination, was reported in different studies (de Miguel et al., 1997 ; Islam et al., 2022 ; Lu et al., 2010 ). On the other hand, Zn compounds are used for preparing dispersant improvers for lubricating oils, used in motor vehicles (de Miguel et al., 1997 ). Scientists(Kabir et al., 2021 ) stated that fuel consumption, oil and lubricant leakage, engine and other vehicle component wear and tear, and exhaust emissions are all potential sources of Cu, Pb, Cd, and Ni. Metal processing, welding, and electronic component manufacturing industries can emit Cu and Ni directly or indirectly, while acrylic flakes can contribute to Pb and Cd pollution in street dust. PCA along with the scree plot represents the variance, moreover, the contribution level of parameters in the two defined dimensions. 3.4. Source apportionment of heavy metals using positive matrix factorization (PMF) model A PMF model was used for source apportionment to identify and quantify the sources of heavy metals in road dust (Fig. 7 ). The model parameters were set to ensure reasonable results, with the minimum value Q controlling the residual matrix E. A factor of four was determined to be the most feasible based on the above adjustments. All model components have signal-to-noise (S/N) values of more than 2, indicating "good" data quality. The Q values were the most stable and had the smallest values when there were four factors and 20 model runs. According to the results of the modeling, the model could be modified. The base run might be deemed stable if all of the elements scaled residual values were between − 3 and + 3 and there was little variation between Q true and Q robust . It is possible to assess how well the model describes each unique species by comparing the observed and predicted metal concentrations. Species should be left out of the model if there is a weak negative connection between observed and predicted values. All elements had fitted r 2 values greater than 0.70, with the exception of Cu, Ni, Fe, and Pb, whose values were 0.47, 0.55, 0.57, and 0.39, respectively. For Mn, Na, and Ca, these values even reached 0.99, 0.94, and 0.97. According to the PMF run findings (Fig. 7 ), factor 1 had a lower weighting (17%), with Pb and Cd making up most of the loading elements with contributions of 43% and 27%, respectively. Pb emissions from vehicles are primarily caused by wear and tear rather than fuel combustion, as well as brake wear and lead wheel weight loss are also thought to be significant sources of Pb in the environment. This is true even though trace amounts of Pb are still present in fuels due to the phase-out of leaded gasoline in recent years. Cd is a vital ingredient found in lubricating oil and tires, which have the potential to release Cd into the environment, including road dust (Duan et al., 2020 ; Smichowski et al., 2008 ). The majority (46%) of factor 2 was made up of Ni, Cr, and Mn, which together contributed 89%, 72%, and 68% of the loading. Factor 2 was the local mining and metal processing industry, which dominates the sources of pollution (S. Wang et al., 2019 ). The main sources of Cr, Ni and Mn production are solid waste and sewage sludge from industrial processes. The burning of coal could release fly ash into the atmosphere, which would then deposit metals on road dust (Raja et al., 2014 ). The main reasons for this are the expansion of different kinds of heavy industrial businesses with a variety of applications and the faster industrial development in the region (Akter et al., 2019 ). With Ca and Fe serving as the main loading elements in factor 3 and contributing significantly with 21% and 17%, respectively, it had the highest weighting (10%) of all the factors. Ca content in road dust from places with heavy traffic may be influenced by vehicle movement, coal burning, road pavement materials, and deicing material use (Skorbiłowicz & Skorbiłowicz, 2019 ). The high concentration of Fe in Narayangonj could be linked to agricultural activities such as fertilizer and pesticide, as their usage have become increasingly popular in recent years (Akter et al., 2019 ; Y. Huang et al., 2018 ). Pb and Na were the key loading factors in factor 4, contributing 47% and 46%, respectively, to the factor's higher proportion (27%). Researchers(F. Li et al., 2016 ) also mentioned the sources of heavy metals that is Pb in road dust might come from roadside soil deposited materials from prior use of leaded petrol. On the other hand, Na exhibited considerable fluctuation, which could indicate a more anthropogenic origin (Skorbiłowicz & Skorbiłowicz, 2019 ). Cumulative factor contribution ratios of heavy metal sources in road dust. (b) factor contribution using PMF 3.5. Environmental pollution risk assessment Various indicators, such as EF, I geo , and CF analyses, profoundly assessed the pollution risk assessment caused by heavy metal contamination in road dust. Toxic metals are regularly discharged into the terrestrial environment as a result of uncontrolled urbanization and industrialization, posing a serious danger to ecological and human health (Kabir et al., 2021 ). The values of contamination factor (CF) for seven metals are in the following manner: Cd > Cr > Cu > Pb > Ni > Mn > Fe. Table 3 shows that Cr, Cu, Cd are very high contaminated, with the exception of Ni, Pb, which are moderately contaminated. Fe and Mn are low contaminated. Cu are mostly derived from tire abrasion and brake pad wear, whereas Cr, Cd, Ni, and Fe are derived from engine wear and metal component corrosion (Gupta & Arya, 2016 ). In the industrial sector, heavy-duty vehicles are frequently used to carry and unload commodities. Cd, Cu, and Ni are commonly found in small-scale metal-related workshops, building operations, and trash from the battery production business (Gupta & Arya, 2016 ). In this study, the value of Enrichment Factor ( EF) was highest for Cd (78.62), and declined in the following order of Cu (28.37) > Cr (12.43) > Pb (6.45) > Ni (4.51) > Mn (1) > Fe (0.109). The values of EF for Cr and Cd belongs to category 5 or Extremely High Enrichment; for Cu it’s category 4 or very high enriched metals possibly because of the existence of denting vehicle workshops, auto workshops, as well as heavy traffic and the pesticides and fertilizers industry. Similarly, Ni, Pb, and Fe considered as moderately enriched, significantly enriched and minimal enriched metals in street dusts respectively in street dust samples from Narayanganj, Bangladesh. In a separate study (Kabir et al., 2021 ), HMs in SD samples were detected in the following order: Cd (46.54 ± 27.45), > Zn (1.93 ± 0.28) > Cu (1.18 ± 0.40) > Pb (1.07 ± 0.36) > Cr (0.75 ± 0.17) > Ni (0.65 ± 0.20) > As (0.10 ± 0.04). Our study found the increased value in terms of CF for the Cd, Cu and Cr than the aforementioned study. In this study, the Geo-accumulation index (I geo ) was used to estimate the amounts of pollution of potentially dangerous chemicals in street dust samples. I geo 's results are encapsulated in Table 2 . In terms I geo , the order of HMs was following the pattern: Cd > Cu > Cr > Pb > Ni > Mn > Fe, where Cd (4.42), Cu (2.95), Cr (1.76), Pb (0.80), Mn (-1.89) and Fe (-5.05). That I geo readings for Cr and Cd are in the Strongly to Extremely Polluted range, Cu is in the Moderately to Strongly Polluted range, Ni and Pb are in the Unpolluted to Moderately Polluted range, while Fe and Mn are nearly unpolluted. A similar study(Kormoker, Kabir, et al., 2022 ) conducted in Dhaka city that reported the Igeo index in the following order: Cr (0.86 ± 0.09) > Mn (0.47 ± 0.09) > Fe (0.37 ± 0.03). In comparing to the aforementioned study, the average I geo value for Cr is much higher in the current study, however, the Mn and Fe were defined as the practically unpolluted category. Table 3 Pollution indices from heavy metals in the street dust samples Contamination factor (CF) Enrichment factor (EF) Geo accumulation index (I geo ) Metal Mean Comment Mean Comment Mean Comment Cr 27.92 Very High Degree 12.43 Significant Enrichment 1.76 moderately polluted Cu 12.03 Very High Degree 28.37 Very high Enrichment 2.95 moderately to strongly polluted Ni 0.85 Low Degree 4.51 Moderate Enrichment 0.29 unpolluted to moderately polluted Cd 31.82 Very High Degree 78.62 Extremely high Enrichment 4.42 strongly to extremely polluted Pb 2.63 Moderate Degree 6.45 Significant Enrichment 0.80 unpolluted to moderately polluted Fe 0.02 Low Degree 0.109 Minimal Enrichment -5.05 practically unpolluted Mn 0.41 Low Degree 1 Minimal Enrichment -1.89 practically unpolluted 3.6. Human health risks assessment In the study, the carcinogenic and non-carcinogenic risks (NCR) were identified for each toxic metals. Exposures from two different age groups (children, and adult) for each site. Dermal contact, inhalation and ingestion of dust particles could pose potential NCR for the exposures (Idris et al., 2020 ; Kabir et al., 2021 ; Zheng et al., 2010 ). Between them, the children mostly affected from the heavy metal contamination since their body weight is comparatively lower, as well as a lower pollution tolerance level make them much more sensitive to toxic components of street dust(Kormoker, Kabir, et al., 2022 ; Zhao et al., 2014 ). The potential NCR is determined by calculating the hazard quotient (HQ) (Proshad, 2019 ). The individual non-carcinogenic HQ and total non-carcinogenic hazard index (HI) of the six dust heavy metals in the Narayanganj Sadar region throughout studied exposure routes are calculated using Equations (1.8) and (1.7), respectively. The HQ values of certain heavy metals for children and adults for NCR are shown in Table S3. For children (6 years old), the hazard index is Cr > Pb > Cd > Mn > Cu > Ni, while for adults (> 24 years), it is Cr > Pb > Cd > Mn > Cu > Ni (Table S3). Among the three exposure modalities, ingestion > skin contact > inhalation revealed the highest non-carcinogenic risk of dust fall heavy metal elements to the human body. The HI > 1 indicates the NCR for the children and adults. Among the six elements, only Cr contributed HI > 1 for the children (1.67). However, most of the elements were below the threshold level, that indicates the NCR potential for the dwellers comparatively lower. The NCR potential was recorded higher for the children than the adult category for Mn and Cr. Moreover, sampling site 1, 15, 21 1nd 24 contributed maximum NCR in the study. Similar report(Kabir et al., 2021 ; Qadeer et al., 2020 ) mentioned the negligible NCR to the adult and children. Nevertheless, the study also found the NCR for children is significantly higher than the adult, that is congruent to the results from different studies conducted in many locations (Chen et al., 2016 ; Kormoker, Kabir, et al., 2022 ; Qadeer et al., 2020 ). Furthermore, our study reports the carcinogenic risk (CR) for the exposure’s ingestion and inhalation, that presented in the Table S3. CR for ingestion was found between the range of 3.74E-06 to 1.04E-03. However, the CR for ingestion is considerably higher (1.04E-03 and 1.12E-04 for child and adult, respectively) for the Cr than the threshold level mentioned by the United States Environmental Protection Agency (1989)(USEPA, 2004). When it comes to assessing human health when exposed to heavy metals, health risk evaluation is vital. Furthermore, the study identified that the most significant threats were associated with the ingestion pathway. For each contaminant, the study found the ingestion largely responsible for the health-related risks for the exposures. Table S2 shows the average daily (ADD) dose for each metal happening mostly from the through ingestion. The order is maintained as: ADD ingestion > ADD dermal > ADD inhalation . This finding is congruent with the results mentioned by (Kabir et al., 2021 ; Qadeer et al., 2020 ), who reported that the ADD is much higher for ingestion compared with inhalation and dermal contact. 3.7. Limitation of the study While our study has provided valuable insights into the distribution of heavy metals (HMs) within our study area, we must acknowledge certain limitations. Firstly, our research did not investigate the presence of seasonal patterns or fluctuations in contamination levels, which represents a significant gap in our investigation. We primarily focused on winter samples, which, while representative of the season when air quality tends to be at its poorest, did not capture potential seasonal variations. Recognizing this gap, future studies should investigate HM contamination dynamics across different seasons to provide a more comprehensive understanding of pollution patterns. Additionally, it's worth noting that while our study estimated the potential human health impacts based on HM concentrations, further research is essential to validate these estimations. Specific investigations into the health effects related to air quality are necessary to confirm the carcinogenic and non-carcinogenic health impacts. These limitations underscore the need for further research and emphasize the importance of conducting a more extensive examination of HM contamination dynamics across various seasons. 4. Conclusion This study reveals the alarming prevalence of toxic elements in street dust samples from Narayanganj Sadar Upazila. Metal concentrations observed at the studied sites was: Ca > Fe > Cr > Cu > Mn > Na > Mg > Pb > Ni > Cd. Pearson's correlation coefficient analysis determined the correlated parameters that potentially indicated the similar source of origin. Multivariate analyses, including cluster analysis, and principal component analysis suggested various sources of contamination. The positive matrix factorization model identified dominant anthropogenic sources such as manufacturing, metal processing industries, heavy traffic, and fuel burning. Risk assessments, through contamination factor, enrichment factor, and geo-accumulation index, underscored the severity of Cr, Cu, and Cd pollution. Most of the HMs pose a higher degree of pollution level in terms of geo-accumulation index, contamination factor and enrichment factor. Furthermore, the study uncovered non-carcinogenic risks, with children facing heightened threats from toxic elements like Mn and Cr compared to adults. Carcinogenic risks were identified, primarily through Cr ingestion. It's worth noting that the risk potential is substantially higher via ingestion than inhalation or dermal contact. The calculation of the average daily dose highlights the higher doses for both adults and children through ingestion. Our findings stress the urgent need for targeted interventions and regulatory measures to mitigate the substantial health and environmental risks associated with these hazardous contaminants. Declarations Acknowledgement The authors would like to acknowledge the contribution of Mahi Muzammel Rofi and Khaleduzzaman Sourov for their help in sample collection. The authors are thankful to the lab attendants of the Department of Environmental Science and Technology, Jashore University of Science and Technology (JUST). Ethical Approval The authors declare the study to be in compliance with ethical standards, and no human data, tissue, or participants were reported in the research. Consent to Participate All the listed authors have consciously read this manuscript and approved the submission. Any author is not off the list of those who can be considered potential contributors. Consent to Publish Not applicable Author’s contribution Md. Hasibur Rahaman: Conceptualization, Investigation, Resources, Review and editing, Data curation, Software, Supervision. Md. Alinur Rahman: Methodology, Data curation, Validation, Visualization, Writing- original draft. Rahamoni Khanam: Formal analysis, Review and editing. Minhaz Ahmed, Md. Sayedul Islam: Methodology, Validation, Formal analysis. Md. Akteruzzaman, Fahim Muntasir Rabbi, Md. Kamrul Hasan: Visualization, Review and editing. Sohely Asharof, Nahid Hasan, Towhida Jahan: Formal analysis, Review and editing. Pragga Chowdhury, Partha Chandra Debnath: Methodology. Tusar Kumar Das: Review and editing. Mohammad Mahfuzur Rahman: Resources, Software, Review and editing. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflict of interests The authors declare no personal and financial benefit from the research that could impact the reported work in this paper. References Adnan M, Xiao B, Xiao P, Zhao P, Li R, Bibi S (2022) Research Progress on Heavy Metals Pollution in the Soil of Smelting Sites in China. Toxics 10(5):231. https://doi.org/10.3390/toxics10050231 Aguilera A, Bautista F, Gutiérrez-Ruiz M, Ceniceros-Gómez AE, Cejudo R, Goguitchaichvili A (2021) Heavy metal pollution of street dust in the largest city of Mexico, sources and health risk assessment. 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As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3768053","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272138458,"identity":"fa077793-67b5-4794-88c9-23154cf7b66d","order_by":0,"name":"Md. 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and sampling locations of the study\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3768053/v1/e0a97cf08765544fc5b735b6.png"},{"id":51082644,"identity":"fe3b9725-3950-463d-9293-cc268b693aff","added_by":"auto","created_at":"2024-02-13 19:27:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77809,"visible":true,"origin":"","legend":"\u003cp\u003eConcentration of heavy metals from the sampling sites in Narayanganj Sadar Upazila, Bangladesh.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3768053/v1/06643b97cae97f55aca2ae67.png"},{"id":51082640,"identity":"8df5562f-3954-41cd-b1b8-7ae54eff6ad9","added_by":"auto","created_at":"2024-02-13 19:27:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":516169,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of contaminants from each sampling point of the study area\u003c/p\u003e","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-3768053/v1/6bace4720add5d1171c8a1f5.png"},{"id":51082643,"identity":"072d1c70-f34f-4d1c-9b2d-7a0fab5d038f","added_by":"auto","created_at":"2024-02-13 19:27:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":19789,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the parameters of the street dust sample, p values (\u0026lt;0, 0.001, 0.01, 0.05, 0.1, 1) symbols (“***”, “**”, “*”, “.”, “”)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3768053/v1/321aabf15171d6f81901b2db.png"},{"id":51082641,"identity":"efcb7bcb-84aa-40b1-b200-931aaf5f92af","added_by":"auto","created_at":"2024-02-13 19:27:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":11442,"visible":true,"origin":"","legend":"\u003cp\u003e2D Cluster represents the three homogenized cluster depending on the similarities of the sampling areas contamination.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3768053/v1/e3060eb038932821cba511d3.png"},{"id":51082639,"identity":"40b13c54-818c-4381-a3b1-8335ee0bc6a6","added_by":"auto","created_at":"2024-02-13 19:27:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":20523,"visible":true,"origin":"","legend":"\u003cp\u003ePCA along with the scree plot represents the variance, moreover, the contribution level of parameters in the two defined dimensions.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3768053/v1/c8bc4d6261964a3e11b6dab5.png"},{"id":51082638,"identity":"194fb7d1-2efc-4dd6-8400-fa516fd5274e","added_by":"auto","created_at":"2024-02-13 19:27:34","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":348555,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative factor contribution ratios of heavy metal sources in road dust. (b) factor contribution using PMF\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3768053/v1/2d38fdd51157051b0aab3e2b.jpeg"},{"id":55505831,"identity":"f9cce817-16b1-49f2-b30f-330d747c0eec","added_by":"auto","created_at":"2024-04-29 11:39:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1364589,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3768053/v1/4e78bc95-d21d-4764-bcc8-fc21b33f838e.pdf"},{"id":51082645,"identity":"d17d63c1-1819-42c6-9f4f-9be68a6cb308","added_by":"auto","created_at":"2024-02-13 19:27:35","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":74913,"visible":true,"origin":"","legend":"","description":"","filename":"SI3rdSubJESPR.docx","url":"https://assets-eu.researchsquare.com/files/rs-3768053/v1/3655f9dbaa4db4cfc41537da.docx"}],"financialInterests":"","formattedTitle":"Heavy Metal Pollution in Street Dust: A Comprehensive Study on Risk Assessment and Source Identification in a Highly Industrialized Area of Bangladesh","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRapid urbanization produces a vast amount of trash daily due to the increased population (Faisal et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The burning and degradation of these waste materials and emissions from vehicles and manufacturing industries may accumulate heavy metals (HMs) in the environment (Xu et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The fine and ultra-fine particles, aerosols, and suspended chemicals end up in the air and seriously damage the air quality; after a while, these toxic elements settle down on the surface with precipitation and damage the soil quality (Faisal et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The anthropogenic sources of HM contamination can mainly be categorized as industrial, agricultural, and sewage pollution (CHEN Shibao, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; El-Zeiny \u0026amp; Abd El-Hamid, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, previous studies reported that vehicular emissions are primarily responsible for emitting harmful toxins, potentially dangerous elements, and HMs from the combustion of fossil fuel and consumable materials (e.g., chemicals, lubricants, rubber, and brake pads) (Prokof\u0026rsquo;eva et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, studies reported different anthropogenic sources and factors for HMs in street dust (SD). For instance, Pb, Zn, Cu, and Cr from the atmospheric settlement; Ni from various natural sources; and Cd deposited from the industrial effluents (Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Safiur Rahman et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Pb deposition in roadside dust and soil is caused by the use of Pb-induced gasoline in prior decades. Cu, Zn, and Cd are emitted due to different types of abrasion, motor oil, and industrial pollution. The corrosion of motor vehicle parts' painted layers and chrome plating is thought to be the source of Ni, Cd, and Cr in SD (Gupta \u0026amp; Arya, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Since they have an extended persistence capacity, those elements settled down and recirculated through different physicochemical processes in the ecosystem (ISLAM et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, the accumulation of toxicants within SD serves as both sink and source of HM pollution, which makes SD an indicator for determining the HMs pollution level in the urban area (J. Huang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Urrutia-Goyes et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe concern arises from the issue that it is challenging to control the impairment of soil environmental quality by HMs which pose a great possible threat to the human health and well-beings (Burges et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; El-Zeiny \u0026amp; Abd El-Hamid, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The HMs can enter the environmental systems through different natural pathways (e.g., weathering, erosion, lithogenesis) and enter into the human body through different anthropogenic activities (e.g., smelting)(Adnan et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Masindi \u0026amp; Muedi, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Stafilov et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; W. Wang et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). HMs can damage the liver and respiratory functions, the cardiovascular and hematopoietic systems, and the gastrointestinal and endocrine systems (Faisal et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Safiur Rahman et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The minute particles, like aerosols and street dust, possess the alarming ability to swiftly infiltrate the human body through ingestion, inhalation, or dermal contact, exacting a profound toll on human health, with children being especially vulnerable, while simultaneously wreaking havoc on the delicate balance of our environment (Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sezgin et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Since the intrusion of HMs into the human body from surrounding exposure cannot be easily minimized, preventing the emission from sources is an efficient control measure. Henceforth, identifying the emission source, migration path, and potential HMs risk to public health and the ecosystem is essential (Sun et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previously, receptor models, such as principal component analysis, cluster analysis, and factor analysis, were applied for source identification. However, these models fail to access the non-negative results below the detection limit (Y. Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Paatero \u0026amp; Tapper, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To overcome the limitations, the positive matrix factorization (PMF) model was used (Fei et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Guan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Paatero, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, the application of geospatial techniques along with remote sensing is efficient for representing the distribution of contaminants levels in the spatial map of the locations, which supports identifying the sources and monitoring the contribution of each component (El-Zeiny et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; El-Zeiny \u0026amp; Abd El-Hamid, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Effects of HMs polluted street dust on the health of city dwellers were well reported in many developed and rapidly urbanized countries, such as China, Spain, Poland, Greece, and Hong Kong (Delgado-Iniesta et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wei et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zgłobicki \u0026amp; Telecka, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Bangladesh, some studies were also conducted in the Dhaka, Kustia-Jhenaidah region, and Gazipur cities that reported the human and ecological risks from the HMs in SD (Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kormoker, Kabir, et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Applying multivariate analyses (Kabir et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported that industrial and vehicular emissions are largely responsible for HMs deposition in SD, by conducting a study in the Kustia-Jhenaidah highway, Bangladesh. (Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) ) reported significant ecological risks; however, they found insignificant carcinogenic and non-carcinogenic risks for humans. Additionally, their study indicated that children are more vulnerable than adults. In other study (Kormoker, Kabir, et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported the potential noncarcinogenic risks for adults and Childs. However, there is a significant research gap about the HMs contamination in terms of SD in Bangladesh and their source identifications.\u003c/p\u003e \u003cp\u003eNarayanganj, the central part of Bangladesh, comprises many world-class jute mills and textile industries, making it a regional industrial center (Noman et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, country's largest river port belongs to the Narayanganj city area, popularly known as the \"Dundee of East.\" Consequently, in many manufacturing industries, on highways, and in planned and unplanned settlements, immense crowding is common nowadays. For facilitating the transportation of people and goods, the rapid growth in the number of vehicles has congested the roads and highways (Seddique et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The engine fuel-burning emissions from the metal processing industries may impair the air quality and thoughtfully contribute to HM pollution. However, there has yet to be conducted a study on Narayanganj City to address the ecological, environmental and human health risks from HMs pollution. Therefore, it is crucial to characterize the HMs from the SD in different point locations (e.g., industrial, residential, bus stoppage, and market area) and measure the potential ecological and human health risks. Since no study mentioned the issues in the Narayanganj Sadar area, the current study aimed to determine the spatial distribution of HMs using geospatial analysis, possible source identification using multivariate analyses, and the ecological and human health risks by applying numerous health indices. That could serve as reference data for further research. Moreover, it could help in national policy-making and the implementation of development projects.\u003c/p\u003e"},{"header":"2. Method and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area\u003c/h2\u003e \u003cp\u003eNarayanganj is a former Dhaka sub-divisional town, and Bangladesh's oldest and most famous river port has evolved into a trade and commerce center. That is, it is bounded on the north by the districts of Gazipur and Narsingdi, on the east by the districts of Brahmanbaria and Comilla, on the south by the district of Munshiganj, and on the west by the district of Dhaka. The district is between 23'33\" and 23'57\" north latitude and 90'26\" to 90'45\" east longitude. It covers a land area of 760 km\u003csup\u003e2\u003c/sup\u003e. The yearly temperature varies between 17.6 and 29.8˚C, and the annual rainfall was recorded at 376 mm in 2011. Some non-tidal rivers (such as the Meghna, Dhaleshwari, Buriganga, and Balu) cross the district, facilitating water supply, drainage, and irrigation (BBS, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study was conducted at thirty specific sampling points (more specifications of the sampling points can be found in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) of the street in the Narayanganj Sadar Upazila, which has 1.5\u0026nbsp;million inhabitants and lies about 20 kilometers southeast of Dhaka city. Rapid growth in the garment industries, global trading (import and export), brickfields, knitwear garments, shipyards, and other commercial activities are annihilating the environmental condition in that region (Md. A. Rahman et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The study collected dust samples from different roadside points, as pointed out in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSampling area, and sampling locations of the study\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sample collection\u003c/h2\u003e \u003cp\u003eThe sampling stations were chosen based on the importance of the sites, such as population density, traffic loads, surrounding land use patterns, presumed dust quality, and pollution extent. Since less rainfall occurs during the dry season, February was selected for the entire sample collection period (Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the Narayanganj Sadar Upazila, a total of 30 cumulative roadside dust samples were collected from various sampling points. With the help of a clean broom and a plastic packer, 500 g of dust samples were gathered from each location. We collected four dust samples at a distance of 0\u0026ndash;10 m from each side of the road to make a composite sample. All the information about the sample location is given in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sample Preparation and Analysis\u003c/h2\u003e \u003cp\u003eIn the laboratory, all samples were air-dried for a week (Safiur Rahman et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The samples were further dried in an oven for 24 hours at 85\u0026deg;C. A stainless steel sieve with a mesh size of 75\u0026micro;m was used to sift the samples for small particles. This particular size was chosen for study because it can readily accumulate a high concentration of heavy metals, be easily resuspended, and be inhaled through the nose or mouth during breathing (Bisht et al., 2022). Nitric acid (HNO\u003csub\u003e3\u003c/sub\u003e) and perchloric acid (HClO\u003csub\u003e4\u003c/sub\u003e) were used to acid digest the 1 g dust sample. The Inductive Coupled Plasma (Agilent- 5800 ICP-OES, USA), and Adsorption Atomic Spectroscopy instrument (AAS Instrument Model: PinAAcle 900H, Perkin Elmer, Waltham, Massachusetts, USA) were used to assess the presence of metals (Taiwo et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) such as Cr, Cu, Ni, Cd, Pb, Fe, Mn, Na, Ca, and Mg.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Quality assurance\u003c/h2\u003e \u003cp\u003eUsing reagent blanks and sample replication, an analytical approach was used to measure precision and bias. The research revealed that the bias and accuracy were both under 10%. Plastic and glassware were marinated in 2% HNO\u003csub\u003e3\u003c/sub\u003e for 24 hours before being cleaned with detergent, rinsed entirely with tap and distilled water, then oven-dried at 37\u0026deg;C before use. Accuracy in the ICP-OES and AAS calibration curves were maintained through the careful selection of standards and instrument optimization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe descriptive statistical analyses were performed using R Studio desktop V-1.3.1093. We got the assumption about the normality of the dataset using the Kolmogorov-Smirnov (K-S) test. After that, we conducted a one-way analysis of variance (ANOVA) to compare the variation among the sampling locations for each parameter. Pearson parametric correlation was applied to assess the relationship among the HMs in different sampling locations. Multivariate analyses such as principal component analysis (PCA), cluster analysis (CA), and dendrograms were performed using suitable packages in R Studio. Moreover, the PMF model was used to determine several factors performed by the EPA PMF-5.0 software. The spatial distributions of investigated heavy metals such as Cr, Cu, Ni, Cd, Pb, Fe, Mn, Na, Ca, and Mg in Narayanganj Sadar road dust samples are obtained using ArcGIS software (Version 10.5, USA). Critical ecological, environmental, and human health risk assessment indices were estimated using mathematical formulas as delineated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and according to the previous studies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePollution risk indices for ecological, environmental and human health risk assessment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental and Ecological risk assessment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThe enrichment factor (EF)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{E}\\text{F}=\\frac{\\left( \\frac{{\\text{C}}_{\\text{n}}}{{\\text{C}}_{\\text{r}\\text{e}\\text{f}}}\\right)\\text{s}\\text{a}\\text{m}\\text{p}\\text{l}\\text{e}}{\\left(\\frac{{\\text{B}}_{\\text{n}}}{{\\text{B}}_{\\text{r}\\text{e}\\text{f}}}\\right)\\text{b}\\text{a}\\text{c}\\text{k}\\text{g}\\text{r}\\text{o}\\text{u}\\text{n}\\text{d}}\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u0026hellip;. (1.1)\u003c/p\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003en\u003c/b\u003e\u003c/sub\u003e = the concentration of the examined element in the examined environment,\u003c/p\u003e\u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003eref\u003c/b\u003e\u003c/sub\u003e = the concentration of the examined element in the reference environment,\u003c/p\u003e\u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003csub\u003e\u003cb\u003en\u003c/b\u003e\u003c/sub\u003e (background)\u0026thinsp;=\u0026thinsp;the concentration of the reference element in the examined environment,\u003c/p\u003e\u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003csub\u003e\u003cb\u003eref\u003c/b\u003e\u003c/sub\u003e (background)\u0026thinsp;=\u0026thinsp;the concentration of the reference element in the reference environment\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEF\u0026thinsp;\u0026lt;\u0026thinsp;2: minimal enrichment\u003c/p\u003e \u003cp\u003e2\u0026thinsp;\u0026le;\u0026thinsp;EF\u0026thinsp;\u0026lt;\u0026thinsp;5: moderate enrichment\u003c/p\u003e \u003cp\u003e5\u0026thinsp;\u0026le;\u0026thinsp;EF\u0026thinsp;\u0026lt;\u0026thinsp;20: significant enrichment\u003c/p\u003e \u003cp\u003e20\u0026thinsp;\u0026le;\u0026thinsp;EF\u0026thinsp;\u0026lt;\u0026thinsp;40: very high enrichment\u003c/p\u003e \u003cp\u003eEF\u0026thinsp;\u0026ge;\u0026thinsp;40: extremely high enrichment.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[38] [39]\u003c/p\u003e \u003cp\u003e[8]\u003c/p\u003e \u003cp\u003e[40] [41] [42] ; [43].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeo-accumulation Index (l\u003c/b\u003e\u003csub\u003e\u003cb\u003egeo\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mathbf{I}}_{\\mathbf{g}\\mathbf{e}\\mathbf{o} = {\\mathbf{log}}_{2}({\\mathbf{C}}_{\\mathbf{n}/1.5{\\mathbf{B}}_{\\mathbf{n} )}}}\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (1.2)\u003c/p\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003en\u003c/b\u003e\u003c/sub\u003e = The measured concentration of metal (n) in the soil\u003c/p\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003csub\u003e\u003cb\u003en\u003c/b\u003e\u003c/sub\u003e = The background value of element (n) in the background sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgeo\u0026thinsp;\u0026le;\u0026thinsp;0: practically unpolluted\u003c/p\u003e \u003cp\u003e0\u0026thinsp;\u0026le;\u0026thinsp;Igeo\u0026thinsp;\u0026le;\u0026thinsp;1: unpolluted to moderately polluted\u003c/p\u003e \u003cp\u003e1\u0026thinsp;\u0026le;\u0026thinsp;Igeo\u0026thinsp;\u0026le;\u0026thinsp;2: moderately polluted\u003c/p\u003e \u003cp\u003e2\u0026thinsp;\u0026le;\u0026thinsp;Igeo\u0026thinsp;\u0026le;\u0026thinsp;3: moderately to strongly polluted\u003c/p\u003e \u003cp\u003e3\u0026thinsp;\u0026le;\u0026thinsp;Igeo\u0026thinsp;\u0026le;\u0026thinsp;4: strongly polluted\u003c/p\u003e \u003cp\u003e4\u0026thinsp;\u0026le;\u0026thinsp;Igeo\u0026thinsp;\u0026le;\u0026thinsp;5: strongly to extremely polluted\u003c/p\u003e \u003cp\u003eIgeo\u0026thinsp;\u0026gt;\u0026thinsp;5: extremely polluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[38] [44]\u003c/p\u003e \u003cp\u003e[39]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContamination Factor (CF)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{C}\\varvec{F} = \\frac{{\\varvec{C}}_{\\varvec{m}\\varvec{e}\\varvec{t}\\varvec{a}\\varvec{l}}}{{\\varvec{C}}_{\\varvec{b}\\varvec{a}\\varvec{c}\\varvec{k}\\varvec{g}\\varvec{r}\\varvec{o}\\varvec{u}\\varvec{n}\\varvec{d}}}\\)\u003c/span\u003e\u003c/span\u003e .\u003cb\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u003c/b\u003e (1.3)\u003c/p\u003e \u003cp\u003eC\u003csub\u003emetal\u003c/sub\u003e = Concentration of heavy metals\u003c/p\u003e \u003cp\u003eC\u003csub\u003ebackground\u003c/sub\u003e = Concentration of background values of heavy metals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCF\u0026thinsp;\u0026lt;\u0026thinsp;1: Low degree\u003c/p\u003e \u003cp\u003e1\u0026thinsp;\u0026le;\u0026thinsp;CF\u0026thinsp;\u0026lt;\u0026thinsp;3: Moderate degree\u003c/p\u003e \u003cp\u003e3\u0026thinsp;\u0026le;\u0026thinsp;CF\u0026thinsp;\u0026lt;\u0026thinsp;6: Considerable degree\u003c/p\u003e \u003cp\u003eCF\u0026thinsp;\u0026ge;\u0026thinsp;6: Very high degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[38]\u003c/p\u003e \u003cp\u003e[44]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHuman health risk methodology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThe average daily dose (ADD) of each heavy metal (mg kg\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eday\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{A}\\text{D}\\text{D}\\left(\\text{i}\\text{n}\\text{g}\\right)=\\frac{ \\text{C} \\times \\text{I}\\text{n}\\text{g}\\text{R} \\times \\text{E}\\text{F} \\times \\text{E}\\text{D} }{\\text{B}\\text{W} \\times \\text{A}\\text{T}}\\times {10}^{-6}\\)\u003c/span\u003e\u003c/span\u003e ... \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (1.4)\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{A}\\text{D}\\text{D}\\left(\\text{i}\\text{n}\\text{h}\\right) = \\frac{\\text{C} \\times \\text{I}\\text{n}\\text{h}\\text{R} \\times \\text{E}\\text{F} \\times \\text{E}\\text{D}}{\\text{P}\\text{E}\\text{F} \\times \\text{B}\\text{W} \\times \\text{A}\\text{T}}\\)\u003c/span\u003e\u003c/span\u003e \u0026hellip;\u0026hellip;. (1.5)\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{A}\\text{D}\\text{D}\\left(\\text{d}\\text{e}\\text{r}\\text{m}\\text{a}\\text{l}\\right) = \\frac{\\text{C}\\times \\text{S}\\text{A} \\times \\text{A}\\text{F} \\times \\text{A}\\text{B}\\text{F}\\times \\text{E}\\text{F}\\times \\text{E}\\text{D}}{\\text{B}\\text{W}\\times \\text{A}\\text{T}}\\times {10}^{-6}\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003cp\u003e(Child)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003cp\u003e(Adult)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIngR\u0026thinsp;=\u0026thinsp;Ingestion rate (mg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInhR\u0026thinsp;=\u0026thinsp;Inhalation rate (m\u003csup\u003e3\u003c/sup\u003e/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEF\u0026thinsp;=\u0026thinsp;Particle Emission Factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSA\u0026thinsp;=\u0026thinsp;Surface of exposed skin area (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABF\u0026thinsp;=\u0026thinsp;Dermal Absorption Factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF\u0026thinsp;=\u0026thinsp;Skin Adherence Factor(mg/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eED\u0026thinsp;=\u0026thinsp;Duration of Exposure (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u0026thinsp;=\u0026thinsp;Frequency of Exposure (days/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT\u0026thinsp;=\u0026thinsp;Average Time Non- Carcinogens(days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eED*365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eED*365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70*365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70*365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW\u0026thinsp;=\u0026thinsp;Body Weight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[45] [41]\u003c/p\u003e \u003cp\u003e[40] [46]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHazard index (HI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eReference and slope factor of metals\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{H}\\text{I}=\\left(\\text{H}\\text{Q}\\right)\\text{i}\\text{n}\\text{g}+\\left(\\text{H}\\text{Q}\\right)\\text{i}\\text{n}\\text{h}+\\left(\\text{H}\\text{Q}\\right)\\text{d}\\text{e}\\text{r}\\text{m}\\text{a}\\text{l}\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u0026hellip;\u0026hellip;\u0026hellip;. (1.7)\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{H}\\text{I}=\\left(\\frac{\\text{A}\\text{D}\\text{D}}{\\text{R}\\text{f}\\text{D}}\\right)\\text{i}\\text{n}\\text{g}\\text{e}\\text{s}\\text{t}\\text{i}\\text{o}\\text{n}+\\left(\\frac{\\text{A}\\text{D}\\text{D}}{\\text{R}\\text{f}\\text{D}}\\right)\\text{i}\\text{n}\\text{h}\\text{a}\\text{l}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}+\\left(\\frac{\\text{A}\\text{D}\\text{D}}{\\text{R}\\text{f}\\text{D}}\\right)\\text{d}\\text{e}\\text{r}\\text{m}\\text{a}\\text{l}\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; (1.8)\u003c/p\u003e \u003cp\u003eThe HQ is the ratio of a metal\u0026rsquo;s ADD to its reference dose (RfD) for exposure pathways\u003c/p\u003e \u003cp\u003ethat are identical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRfD\u003csub\u003eing\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRfD\u003csub\u003einh\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRfD\u003csub\u003edermal\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSF\u003csub\u003einh\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.86E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.00E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.20E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.02E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.06E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.40E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.40E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.30E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.50E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.52E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.25E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.60E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e[21,47]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCarcinogenic Risk (CR) Assessment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(CR=ADD \\times SF\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u0026hellip;\u0026hellip;. (1.9)\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(TCR= \\sum CR\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; (2.0)\u003c/p\u003e \u003cp\u003eADD\u0026thinsp;=\u0026thinsp;The average daily dose of each heavy metal (mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and\u003c/p\u003e \u003cp\u003eSF\u0026thinsp;=\u0026thinsp;is the slope factor (mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003csup\u003e\u0026minus;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTCR\u0026thinsp;=\u0026thinsp;Total Carcinogenic Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePositive Matrix Factorization (PMF)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eX\u003csub\u003eij\u003c/sub\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(=+{\\sum }_{\\text{k}=1}^{\\text{p}}{\\text{g}}_{\\text{i}\\text{k}}{\\text{f}}_{\\text{k}\\text{f}}+ {\\text{e}}_{\\text{i}\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eQ = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{\\text{i}=1 }^{\\text{n}}\\sum _{\\text{j}=1}^{\\text{m}} \\left\\{\\frac{{\\text{e}}_{\\text{i}\\text{j}}}{{\\text{u}}_{\\text{i}\\text{j}}}\\right\\}\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eX\u003csub\u003eij\u003c/sub\u003e = Species j concentration on sample i\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;Number of factors\u003c/p\u003e \u003cp\u003eg\u003csub\u003eik\u003c/sub\u003e= Relevant factor contribution of k to i sample,\u003c/p\u003e \u003cp\u003ef\u003csub\u003ekf\u003c/sub\u003e = Species\u0026rsquo; concentration of j in profile\u003c/p\u003e \u003cp\u003efactor k, and e\u003csub\u003eij\u003c/sub\u003e is the residuals\u003c/p\u003e \u003cp\u003eIf X\u003csub\u003eij\u003c/sub\u003e \u0026le; MDL,\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{U}}_{\\text{i}\\text{j}}=\\frac{5}{6}\\times \\text{M}\\text{D}\\text{L}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eUij\u0026thinsp;=\u0026thinsp;The uncertainty\u003c/p\u003e \u003cp\u003eIf X\u003csub\u003eij\u003c/sub\u003e \u0026ge; MDL,\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{U}\\text{i}\\text{j} = \\sqrt[]{({{\\sigma }\\text{j} \\times {\\left.\\left(\\text{X}\\text{i}\\text{j}\\right.\\right)}^{2} + \\left(\\text{M}\\text{D}\\text{L}\\right)2}_{ }}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eσj\u0026thinsp;=\u0026thinsp;The relative standard deviation\u003c/p\u003e \u003cp\u003eMDL\u0026thinsp;=\u0026thinsp;Minimum detection limit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(MDL) values (mg/kg):\u003c/p\u003e \u003cp\u003eCr: 2\u003c/p\u003e \u003cp\u003eNi: 1\u003c/p\u003e \u003cp\u003eCu:0.6\u003c/p\u003e \u003cp\u003eZn: 1\u003c/p\u003e \u003cp\u003eAs: 0.4\u003c/p\u003e \u003cp\u003eCd: 0.09\u003c/p\u003e \u003cp\u003ePb: 2\u003c/p\u003e \u003cp\u003eHg: 0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[48]\u003c/p\u003e \u003cp\u003e[49]\u003c/p\u003e \u003cp\u003e[21]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Concentration of the contaminants in the street dust samples\u003c/h2\u003e \u003cp\u003eThe descriptive statistics of each element and HMs (Cr, Cu, Cd, Mn, Zn, Fe, Ni, Mg, Ca, and Na) are represented in the following Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The average concentrations of Cr, Cu, Ni, Cd, Pb, Mn, Na, Ca, and Mg were 317.25\u0026thinsp;\u0026plusmn;\u0026thinsp;62.25, 247.86\u0026thinsp;\u0026plusmn;\u0026thinsp;25.76, 53.26\u0026thinsp;\u0026plusmn;\u0026thinsp;16.76, 3.53\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03, 56.35\u0026thinsp;\u0026plusmn;\u0026thinsp;31.76, 443.94\u0026thinsp;\u0026plusmn;\u0026thinsp;6.48, 227.18\u0026thinsp;\u0026plusmn;\u0026thinsp;33.86, 101.74\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79, 4842 203.90, and 79.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70 mgkg\u0026thinsp;\u0026minus;\u0026thinsp;1, respectively. The ten elements were ranged between Cr: 112.46-491.06, Cu: 138.13-261.25, Ni: 14.28\u0026ndash;90.25, Cd: 1.78\u0026ndash;11.75, Pb: 10.53-177.23, Fe: 419.5-462.5, Mn: 94.73-271.25, Na: 93.08-108.68, Ca: 4227.5-5002.5, and Mg: 75-82.68 mg/kg, respectively. The concentration of Cr and Cu are potentially higher than the study (Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)that reported range (Cr: 55.2-122.8, Cu: 26.3\u0026ndash;72.1); moreover, the concentration of Ni, Cd, and Pb are congruent to the detected values of the study. The descriptive statistics of each element are given in Table S2. Another study (Kabir et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted in the Kustia district, Bangladesh, reported values in the range of Cr: 12.2\u0026ndash;19.6, Cu: 58.6-116.2, Ni: 8.2\u0026ndash;22.6, Cd: 0.08\u0026ndash;0.28, and Pb: 32.5\u0026ndash;78.9 mg/kg, respectively, from the street dust samples. However, these values are also significantly lower than the current study areas' concentration. In addition, all the values were compared with the background value, the maximum permissible contents of potentially toxic elements for agricultural soils (PTE-MPC), and the soil invertebrate toxicity reference values (SI-TRV). The average concentration of Cr, Cu, Ni, and Cd was considerably higher than the PTE-MPC stated reference value (Table S2), indicating that HM contamination poses a potential risk for agricultural production. Moreover, the concentration of Cr and Cu was beyond the SI-TRV, which indicates the risk potential for the soil-living invertebrates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConcentration of heavy metals from the sampling sites in Narayanganj Sadar Upazila, Bangladesh.\u003c/p\u003e \u003cp\u003eA critical consideration from the study is that the Cr and Cu contamination levels are considerably higher than those reported (Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kormoker, Kabir, et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for the SD samples from the other region of Bangladesh. In urban environment, the sources of Cr in the SD could be combustion of fossil fuels, car corrosion, metal works, vehicle scrap workshops, painting and dying, and long-time traffic congestion on the road (Delibašić et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Safiur Rahman et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tan et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In comparing the global perspective, the Cr and Cu concentrations are significantly higher than the stated locations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) around the world that were reported by (Ambade, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Y. Cai \u0026amp; Zhang, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Duzgoren-Aydin et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Jose \u0026amp; Srimuruganandam, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Roy et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Safiur Rahman et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sutherland \u0026amp; Tolosa, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), except the value mentioned by (Banerjee, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In comparison to other regions within Bangladesh, the research found that the elevated levels of heavy metal contamination, thereby emphasizing the pressing environmental concerns within these specific areas (Islam et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kormoker, Idris, et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kormoker, Kabir, et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison with global distribution of heavy metal concentrations (mg/kg) in street dust of Narayanganj Sadar Upazila, Bangladesh.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCities/Country\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNarayanganj Sadar, Bangladesh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e317.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e247.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e53.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e56.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e443.94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e227.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eThis study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDhaka, Bangladesh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Ahmed \u0026amp; Ishiga, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDhaka, Bangladesh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Rakib et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDhaka, Bangladesh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e261.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Safiur Rahman et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelhi, India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e481.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e512.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e597.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Banerjee, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVellore, India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22,059.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e281.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Jose \u0026amp; Srimuruganandam, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBhopal, India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e534.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e708.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Ambade, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcutta, India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Chatterjee \u0026amp; Banerjee, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1999\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNanjing, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Men et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeijing, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e201.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Du et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuangzhou, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21,184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Liang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuangzhou, Southeast\u003c/p\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Duzgoren-Aydin et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToronto, Canada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e182.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48,234.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1407.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Men et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHawaii, USA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Sutherland \u0026amp; Tolosa, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2000\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalmero, Italy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Varrica et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBV-China*\u003c/p\u003e \u003cp\u003e(CNEMC, 1990)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Safiur Rahman et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e*BV-China CNEMC\u003c/b\u003e Background value by China Environmental Centre\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Spatial Distribution of contaminants\u003c/h2\u003e \u003cp\u003eDepending on the contamination level, each element represented in the spatial map in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b, c, d, e, f, g, and h. According to the spatial distribution map of Mn, Fe, Ca, and Cr metals, sampling site N1 (a government school and college in Shampur) was the most polluted by HMs. Sampling sites N16, N5, and N8 are polluted sites for Cr, but relatively less than sampling site N2. Sampling locations N5, N8, and N26 are the textile industry, Kasimpur government primary school, and Shibu market, respectively. In the BISIC area (N7), several sorts of industrial units have sprouted up, are indiscriminately using resources, and have a high concentration of Cr, Ca, Cu, Na, and Mn. The industrial processing, textile and dying could potentially enhance the HMs in the environment (Begum \u0026amp; Huq, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The combustion of lubricant oil dominates the Cr, Cu, Ni and Mn emission, according to the study by (Pulles et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Moreover, the steel production and coal-fired industries might pose a serious risk for HMs contamination (Hu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, the large proportion of Pb emission could occur due to the industrial activities around the roadside (Qu et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the sampling sites N18, N21, N23, and N10, moderate to high level of Mg was assessed. Sampling sites N10, N18, N21, and N23 were the premier cement industry, bazaar, petroleum filling station, and heavy traffic road area, respectively. All types of cement used to construct roads, bridges, and building components contain magnesium as a common component. The majority of Mg was found in the soils that surround roads and highways, particularly those that are located near parks, forests, and other potentially unpolluted regions (Bućko et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Na has a rich concentration at N15, N17, N14, N13, N30, N24, N12, N20, and N24. These are the locations of the following: garments, shops, heavy traffic areas, cinema hall, river port, hospital, and commercial areas, mostly. Pb has a high concentration at N1, N2, N19, N23, N24, and N26. These areas are linked to the Dhaka-Chittagong Highway, Steel manufacturing industrial area, EPZ Narayanganj, Filling Station, and Shibu Market. The concentration of Na in this area was likewise substantially greater in a few of the study locations, including (N10, N15, and N17). Despite the fact that Pb has been banned as an ingredient in gasoline for many years, the quantity of Pb in urban road dust still reflects the considerable amount of previous Pb pollution and the extended half-life of Pb in soils. Some of the Pb in road dust may have come from particles generated by industrial processes (Rawat et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpatial distribution of contaminants from each sampling point of the study area\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Interrelationship of the potential toxic elements in the street dusts in the different sampling locations\u003c/h2\u003e \u003cp\u003ePearson's correlation coefficient was used to determine the degree of correlation between HM concentrations, as well as offer hints about HM sources and routes. Anthropogenic activities are among the most significant factors influencing the relative abundance of HMs (Mitra et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The concentrations of all metals are positively linked at the most contaminated location. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the Pearson's correlation coefficients for metals in 30 road dust samples from the Narayanganj Sadar Upazila. The highest correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were identified between elemental groupings (Cr-Ni), (Cr-Fe), (Cr- Mn), (Cr- Mg), (Ni-Cd), (Ni-Pb), (Ni- Fe), (Ni- Mn), (Ni-Mg), (Cd-Pb), (Fe-Mn), (Fe-Mg), (Mn-Mg) revealing a similar origin for these elements, most likely due to contributions from other elements. The element pair (Cr- Cd), (Cu- Ni), (Cd-Fe), (Cd-Mn), (Cd-Mg), (Pb-Mn) significantly correlated at the point of (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. According to published studies (W. L. Li \u0026amp; Pauluhn, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), if the correlation coefficient between heavy metal components is positive, these factors may have a common root, mutual dependency, and behavior during transportation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCorrelation between the parameters of the street dust sample, p values (\u0026lt;\u0026thinsp;0, 0.001, 0.01, 0.05, 0.1, 1) symbols (\u0026ldquo;***\u0026rdquo;, \u0026ldquo;**\u0026rdquo;, \u0026ldquo;*\u0026rdquo;, \u0026ldquo;.\u0026rdquo;, \u0026ldquo;\u0026rdquo;)\u003c/p\u003e \u003cp\u003eThe results of cluster analysis (CA) presented in the cluster dendrogram (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) and 2D cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) indicate major clusters of the thirty sampling points of the study area. Depending on the similar pattern of pollution indices, industrial settings, traffic volume, and commercial setup, three major clusters were defined in the analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e represents cluster 1, which includes most of the sampling locations: 2 to 9, 13 to 23, 25, 26, 28, and 29. Cluster 2 is composed of two locations: 12 and 29, and cluster 3 is composed of sampling locations 1, 2, 10, 11, 20, 24, 26, 27, and 30. Cluster 1 mainly consists of residential and commercial setups. Among them, locations 2 to 6 were in the residential area, 7 to 15 in the commercial area, and the remaining were mixed industrial, commercial, and residential setups. The two sampling sites of cluster 2 were located entirely in the commercial urban area, and the cluster 3 sampling area was among the residential, commercial, and industrial areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2D Cluster represents the three homogenized cluster depending on the similarities of the sampling areas contamination.\u003c/h3\u003e\n\u003cp\u003eThe findings of PCA revealed that the analyzed heavy metals are well represented by three principal components (PCs), which can explain 73.95% of the total variance (Fig. S2, Table S4). Five elements (Cr, Fe, Mn, Mg, and Ni) dominated the first PC (PC1), which explained 46.9% of the total variation. However, each element depicted an inverse correlation among themselves in PC1. The heavy industrial manufacturing processes, machinery plants could largely responsible for the Cr, Mn and Ni contribution level in the PC1 (Yeung et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In case of the PC2, Cu, Ni, Cd, Pb, and Na contributed positively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and a significant contribution was observed for Cu (Table S4). The element is widely applied for the tire preparation during the vulcanizing process, about 0.4\u0026ndash;4.3% Cu consisting the resulted tire thread (Lu et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Cu is used as corrosive resistance in different mechanical parts, the corrosion and dissolution occurrence could be regarded as a potential source (Lu et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Therefore, the heavy traffic density, and corrosion of tires in busy industrial and commercial areas could be the potential source of the contamination level. According to the importance of the components, PC3's (13.30%) variance showed a similar trend compared to PC2's (13.77%). In the PC3, Cr, Cu, Ni, Fe, Mn, and Mg contributed positively, while a significant negative contribution could be observed for Na. Numerous human induced pollution and atmospheric particle deposition could potentially contribute to different heavy metal addition in the urban settlements, more specifically, Ni, Cu, Cd, Pb, As and Zn could be added in the road dust due to different kinds of emissions (Iqbal \u0026amp; Shah, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Manzoor et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pandey et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Local anthropogenic activities are responsible for Pb, Cu, Ni and Cd contamination, was reported in different studies (de Miguel et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). On the other hand, Zn compounds are used for preparing dispersant improvers for lubricating oils, used in motor vehicles (de Miguel et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Scientists(Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) stated that fuel consumption, oil and lubricant leakage, engine and other vehicle component wear and tear, and exhaust emissions are all potential sources of Cu, Pb, Cd, and Ni. Metal processing, welding, and electronic component manufacturing industries can emit Cu and Ni directly or indirectly, while acrylic flakes can contribute to Pb and Cd pollution in street dust.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePCA along with the scree plot represents the variance, moreover, the contribution level of parameters in the two defined dimensions.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Source apportionment of heavy metals using positive matrix factorization (PMF) model\u003c/h2\u003e \u003cp\u003eA PMF model was used for source apportionment to identify and quantify the sources of heavy metals in road dust (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The model parameters were set to ensure reasonable results, with the minimum value Q controlling the residual matrix E. A factor of four was determined to be the most feasible based on the above adjustments. All model components have signal-to-noise (S/N) values of more than 2, indicating \"good\" data quality. The Q values were the most stable and had the smallest values when there were four factors and 20 model runs. According to the results of the modeling, the model could be modified. The base run might be deemed stable if all of the elements scaled residual values were between \u0026minus;\u0026thinsp;3 and +\u0026thinsp;3 and there was little variation between Q\u003csub\u003etrue\u003c/sub\u003e and Q\u003csub\u003erobust\u003c/sub\u003e. It is possible to assess how well the model describes each unique species by comparing the observed and predicted metal concentrations. Species should be left out of the model if there is a weak negative connection between observed and predicted values. All elements had fitted r\u003csup\u003e2\u003c/sup\u003e values greater than 0.70, with the exception of Cu, Ni, Fe, and Pb, whose values were 0.47, 0.55, 0.57, and 0.39, respectively. For Mn, Na, and Ca, these values even reached 0.99, 0.94, and 0.97. According to the PMF run findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), factor 1 had a lower weighting (17%), with Pb and Cd making up most of the loading elements with contributions of 43% and 27%, respectively. Pb emissions from vehicles are primarily caused by wear and tear rather than fuel combustion, as well as brake wear and lead wheel weight loss are also thought to be significant sources of Pb in the environment. This is true even though trace amounts of Pb are still present in fuels due to the phase-out of leaded gasoline in recent years. Cd is a vital ingredient found in lubricating oil and tires, which have the potential to release Cd into the environment, including road dust (Duan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Smichowski et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The majority (46%) of factor 2 was made up of Ni, Cr, and Mn, which together contributed 89%, 72%, and 68% of the loading. Factor 2 was the local mining and metal processing industry, which dominates the sources of pollution (S. Wang et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The main sources of Cr, Ni and Mn production are solid waste and sewage sludge from industrial processes. The burning of coal could release fly ash into the atmosphere, which would then deposit metals on road dust (Raja et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The main reasons for this are the expansion of different kinds of heavy industrial businesses with a variety of applications and the faster industrial development in the region (Akter et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). With Ca and Fe serving as the main loading elements in factor 3 and contributing significantly with 21% and 17%, respectively, it had the highest weighting (10%) of all the factors. Ca content in road dust from places with heavy traffic may be influenced by vehicle movement, coal burning, road pavement materials, and deicing material use (Skorbiłowicz \u0026amp; Skorbiłowicz, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The high concentration of Fe in Narayangonj could be linked to agricultural activities such as fertilizer and pesticide, as their usage have become increasingly popular in recent years (Akter et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Y. Huang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Pb and Na were the key loading factors in factor 4, contributing 47% and 46%, respectively, to the factor's higher proportion (27%). Researchers(F. Li et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) also mentioned the sources of heavy metals that is Pb in road dust might come from roadside soil deposited materials from prior use of leaded petrol. On the other hand, Na exhibited considerable fluctuation, which could indicate a more anthropogenic origin (Skorbiłowicz \u0026amp; Skorbiłowicz, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCumulative factor contribution ratios of heavy metal sources in road dust. (b) factor contribution using PMF\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Environmental pollution risk assessment\u003c/h2\u003e \u003cp\u003eVarious indicators, such as EF, I\u003csub\u003egeo\u003c/sub\u003e, and CF analyses, profoundly assessed the pollution risk assessment caused by heavy metal contamination in road dust. Toxic metals are regularly discharged into the terrestrial environment as a result of uncontrolled urbanization and industrialization, posing a serious danger to ecological and human health (Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The values of contamination factor (CF) for seven metals are in the following manner: Cd\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Fe. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that Cr, Cu, Cd are very high contaminated, with the exception of Ni, Pb, which are moderately contaminated. Fe and Mn are low contaminated. Cu are mostly derived from tire abrasion and brake pad wear, whereas Cr, Cd, Ni, and Fe are derived from engine wear and metal component corrosion (Gupta \u0026amp; Arya, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the industrial sector, heavy-duty vehicles are frequently used to carry and unload commodities. Cd, Cu, and Ni are commonly found in small-scale metal-related workshops, building operations, and trash from the battery production business (Gupta \u0026amp; Arya, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In this study, the value of Enrichment Factor \u003cb\u003e(\u003c/b\u003eEF) was highest for Cd (78.62), and declined in the following order of Cu (28.37)\u0026thinsp;\u0026gt;\u0026thinsp;Cr (12.43)\u0026thinsp;\u0026gt;\u0026thinsp;Pb (6.45)\u0026thinsp;\u0026gt;\u0026thinsp;Ni (4.51)\u0026thinsp;\u0026gt;\u0026thinsp;Mn (1)\u0026thinsp;\u0026gt;\u0026thinsp;Fe (0.109). The values of EF for Cr and Cd belongs to category 5 or Extremely High Enrichment; for Cu it\u0026rsquo;s category 4 or very high enriched metals possibly because of the existence of denting vehicle workshops, auto workshops, as well as heavy traffic and the pesticides and fertilizers industry. Similarly, Ni, Pb, and Fe considered as moderately enriched, significantly enriched and minimal enriched metals in street dusts respectively in street dust samples from Narayanganj, Bangladesh. In a separate study (Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), HMs in SD samples were detected in the following order: Cd (46.54\u0026thinsp;\u0026plusmn;\u0026thinsp;27.45), \u0026gt; Zn (1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28)\u0026thinsp;\u0026gt;\u0026thinsp;Cu (1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40)\u0026thinsp;\u0026gt;\u0026thinsp;Pb (1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36)\u0026thinsp;\u0026gt;\u0026thinsp;Cr (0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17)\u0026thinsp;\u0026gt;\u0026thinsp;Ni (0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20)\u0026thinsp;\u0026gt;\u0026thinsp;As (0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04). Our study found the increased value in terms of CF for the Cd, Cu and Cr than the aforementioned study. In this study, the Geo-accumulation index (I\u003csub\u003egeo\u003c/sub\u003e) was used to estimate the amounts of pollution of potentially dangerous chemicals in street dust samples. I\u003csub\u003egeo\u003c/sub\u003e's results are encapsulated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In terms I\u003csub\u003egeo\u003c/sub\u003e, the order of HMs was following the pattern: Cd\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Fe, where Cd (4.42), Cu (2.95), Cr (1.76), Pb (0.80), Mn (-1.89) and Fe (-5.05). That I\u003csub\u003egeo\u003c/sub\u003e readings for Cr and Cd are in the Strongly to Extremely Polluted range, Cu is in the Moderately to Strongly Polluted range, Ni and Pb are in the Unpolluted to Moderately Polluted range, while Fe and Mn are nearly unpolluted. A similar study(Kormoker, Kabir, et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted in Dhaka city that reported the Igeo index in the following order: Cr (0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09)\u0026thinsp;\u0026gt;\u0026thinsp;Mn (0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09)\u0026thinsp;\u0026gt;\u0026thinsp;Fe (0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03). In comparing to the aforementioned study, the average I\u003csub\u003egeo\u003c/sub\u003e value for Cr is much higher in the current study, however, the Mn and Fe were defined as the practically unpolluted category.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePollution indices from heavy metals in the street dust samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eContamination factor (CF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eEnrichment factor (EF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eGeo accumulation index (I\u003csub\u003egeo\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eComment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant Enrichment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003emoderately polluted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVery high Enrichment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003emoderately to strongly polluted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate Enrichment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunpolluted to moderately polluted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExtremely high Enrichment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003estrongly to extremely polluted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant Enrichment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunpolluted to moderately polluted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinimal Enrichment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003epractically unpolluted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinimal Enrichment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003epractically unpolluted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Human health risks assessment\u003c/h2\u003e \u003cp\u003eIn the study, the carcinogenic and non-carcinogenic risks (NCR) were identified for each toxic metals. Exposures from two different age groups (children, and adult) for each site. Dermal contact, inhalation and ingestion of dust particles could pose potential NCR for the exposures (Idris et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Between them, the children mostly affected from the heavy metal contamination since their body weight is comparatively lower, as well as a lower pollution tolerance level make them much more sensitive to toxic components of street dust(Kormoker, Kabir, et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The potential NCR is determined by calculating the hazard quotient (HQ) (Proshad, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The individual non-carcinogenic HQ and total non-carcinogenic hazard index (HI) of the six dust heavy metals in the Narayanganj Sadar region throughout studied exposure routes are calculated using Equations (1.8) and (1.7), respectively. The HQ values of certain heavy metals for children and adults for NCR are shown in Table S3. For children (6 years old), the hazard index is Cr\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Cd\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Ni, while for adults (\u0026gt;\u0026thinsp;24 years), it is Cr\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Cd\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Ni (Table S3). Among the three exposure modalities, ingestion\u0026thinsp;\u0026gt;\u0026thinsp;skin contact\u0026thinsp;\u0026gt;\u0026thinsp;inhalation revealed the highest non-carcinogenic risk of dust fall heavy metal elements to the human body. The HI\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates the NCR for the children and adults. Among the six elements, only Cr contributed HI\u0026thinsp;\u0026gt;\u0026thinsp;1 for the children (1.67). However, most of the elements were below the threshold level, that indicates the NCR potential for the dwellers comparatively lower. The NCR potential was recorded higher for the children than the adult category for Mn and Cr. Moreover, sampling site 1, 15, 21 1nd 24 contributed maximum NCR in the study. Similar report(Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Qadeer et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) mentioned the negligible NCR to the adult and children. Nevertheless, the study also found the NCR for children is significantly higher than the adult, that is congruent to the results from different studies conducted in many locations (Chen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kormoker, Kabir, et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Qadeer et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, our study reports the carcinogenic risk (CR) for the exposure\u0026rsquo;s ingestion and inhalation, that presented in the Table S3. CR for ingestion was found between the range of 3.74E-06 to 1.04E-03. However, the CR for ingestion is considerably higher (1.04E-03 and 1.12E-04 for child and adult, respectively) for the Cr than the threshold level mentioned by the United States Environmental Protection Agency (1989)(USEPA, 2004). When it comes to assessing human health when exposed to heavy metals, health risk evaluation is vital. Furthermore, the study identified that the most significant threats were associated with the ingestion pathway. For each contaminant, the study found the ingestion largely responsible for the health-related risks for the exposures. Table S2 shows the average daily (ADD) dose for each metal happening mostly from the through ingestion. The order is maintained as: ADD\u003csub\u003eingestion\u003c/sub\u003e\u0026gt; ADD\u003csub\u003edermal\u003c/sub\u003e\u0026gt; ADD\u003csub\u003einhalation\u003c/sub\u003e. This finding is congruent with the results mentioned by (Kabir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Qadeer et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who reported that the ADD is much higher for ingestion compared with inhalation and dermal contact.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7. \u003cb\u003eLimitation of the study\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eWhile our study has provided valuable insights into the distribution of heavy metals (HMs) within our study area, we must acknowledge certain limitations. Firstly, our research did not investigate the presence of seasonal patterns or fluctuations in contamination levels, which represents a significant gap in our investigation. We primarily focused on winter samples, which, while representative of the season when air quality tends to be at its poorest, did not capture potential seasonal variations. Recognizing this gap, future studies should investigate HM contamination dynamics across different seasons to provide a more comprehensive understanding of pollution patterns. Additionally, it's worth noting that while our study estimated the potential human health impacts based on HM concentrations, further research is essential to validate these estimations. Specific investigations into the health effects related to air quality are necessary to confirm the carcinogenic and non-carcinogenic health impacts. These limitations underscore the need for further research and emphasize the importance of conducting a more extensive examination of HM contamination dynamics across various seasons.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study reveals the alarming prevalence of toxic elements in street dust samples from Narayanganj Sadar Upazila. Metal concentrations observed at the studied sites was: Ca\u0026thinsp;\u0026gt;\u0026thinsp;Fe\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Na\u0026thinsp;\u0026gt;\u0026thinsp;Mg\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Cd. Pearson's correlation coefficient analysis determined the correlated parameters that potentially indicated the similar source of origin. Multivariate analyses, including cluster analysis, and principal component analysis suggested various sources of contamination. The positive matrix factorization model identified dominant anthropogenic sources such as manufacturing, metal processing industries, heavy traffic, and fuel burning. Risk assessments, through contamination factor, enrichment factor, and geo-accumulation index, underscored the severity of Cr, Cu, and Cd pollution. Most of the HMs pose a higher degree of pollution level in terms of geo-accumulation index, contamination factor and enrichment factor. Furthermore, the study uncovered non-carcinogenic risks, with children facing heightened threats from toxic elements like Mn and Cr compared to adults. Carcinogenic risks were identified, primarily through Cr ingestion. It's worth noting that the risk potential is substantially higher via ingestion than inhalation or dermal contact. The calculation of the average daily dose highlights the higher doses for both adults and children through ingestion. Our findings stress the urgent need for targeted interventions and regulatory measures to mitigate the substantial health and environmental risks associated with these hazardous contaminants.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the contribution of Mahi Muzammel Rofi and Khaleduzzaman Sourov for their help in sample collection. The authors are thankful to the lab attendants of the Department of Environmental Science and Technology, Jashore University of Science and Technology (JUST).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare the study to be in compliance with ethical standards, and no human data, tissue, or participants were reported in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the listed authors have consciously read this manuscript and approved the submission. Any author is not off the list of those who can be considered potential contributors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMd. Hasibur Rahaman:\u003c/strong\u003e Conceptualization, Investigation, Resources, Review and editing, Data curation, Software, Supervision. \u003cstrong\u003eMd. Alinur Rahman:\u003c/strong\u003e Methodology, Data curation, Validation, Visualization, Writing- original draft. \u003cstrong\u003eRahamoni Khanam:\u003c/strong\u003e Formal analysis, Review and editing. \u003cstrong\u003eMinhaz Ahmed, Md. Sayedul Islam:\u003c/strong\u003e Methodology, Validation, Formal analysis. \u003cstrong\u003eMd. Akteruzzaman, Fahim Muntasir Rabbi, Md. Kamrul Hasan:\u003c/strong\u003e Visualization, Review and editing. \u003cstrong\u003eSohely Asharof, Nahid Hasan, Towhida Jahan:\u003c/strong\u003e Formal analysis, Review and editing. \u003cstrong\u003ePragga Chowdhury, Partha Chandra Debnath:\u003c/strong\u003e Methodology. \u003cstrong\u003eTusar Kumar Das:\u003c/strong\u003e Review and editing. \u003cstrong\u003eMohammad Mahfuzur Rahman:\u003c/strong\u003e Resources, Software, Review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no personal and financial benefit from the research that could impact the reported work in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdnan M, Xiao B, Xiao P, Zhao P, Li R, Bibi S (2022) Research Progress on Heavy Metals Pollution in the Soil of Smelting Sites in China. 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Sci Total Environ 408(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2009.10.075\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2009.10.075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Street dust, heavy metals, health risk, environmental risk, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-3768053/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3768053/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study is intended to report the level of heavy metals (HMs) contamination, their potential source, and their impacts by analyzing street dust (SD) samples collected from thirty distinct sampling locations in Narayanganj Sadar Upazila, Bangladesh. The results suggest that the average concentrations of Chromium (Cr), Copper (Cu), Nickel (Ni), Cadmium (Cd), Lead (Pb), Manganese (Mn), Sodium (Na), Calcium (Ca), and Magnesium (Mg) were 317.25\u0026thinsp;\u0026plusmn;\u0026thinsp;62.25, 247.86\u0026thinsp;\u0026plusmn;\u0026thinsp;25.76, 53.26\u0026thinsp;\u0026plusmn;\u0026thinsp;16.76, 3.53\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03, 56.35\u0026thinsp;\u0026plusmn;\u0026thinsp;31.76, 443.94\u0026thinsp;\u0026plusmn;\u0026thinsp;6.48, 227.18\u0026thinsp;\u0026plusmn;\u0026thinsp;33.86, 101.74\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79, 4842\u0026thinsp;\u0026plusmn;\u0026thinsp;203.90, and 79.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Both Cr and Cu levels were over five and ten times higher than the background values, respectively. Principal component analysis (PCA) and positive matrix factorization (PMF) suggest that industrial activities and heavy traffic on the street could be the potential sources. Moreover, Cr, Cu, and Cd all exhibit 'very high\u0026rsquo; contamination factors (CF), with corresponding enrichment factors (EF) categorized as 'significant', 'very high\u0026rsquo;, and 'high', respectively. The geo-accumulation index (I\u003csub\u003egeo\u003c/sub\u003e) found a moderately to strongly polluted category for Cu and a strong to extremely polluted category for Cd. Risk indices indicate that potential carcinogenic and non-carcinogenic risks were notably higher for children compared to adults, with the primary mode of exposure being ingestion.\u003c/p\u003e","manuscriptTitle":"Heavy Metal Pollution in Street Dust: A Comprehensive Study on Risk Assessment and Source Identification in a Highly Industrialized Area of Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-13 19:27:18","doi":"10.21203/rs.3.rs-3768053/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17217546-8c7d-4771-baef-51c11cbbab3d","owner":[],"postedDate":"February 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-29T11:31:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-13 19:27:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3768053","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3768053","identity":"rs-3768053","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}