Spatial distribution, pollution levels, and risk assessment of potentially toxic metals in road dust from major tourist city, Dehradun, Uttarakhand India

Gupta, Vidhu; Bisht, Lalita; Deep, Akash; Gautam, Sneha

doi:10.1007/s00477-022-02207-0

Cited by 25 publications

(16 citation statements)

References 69 publications

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“…Pb and Cu were the second and third largest pollutants in the study area. Pb and Cu pollution were mainly observed in the city center, which is associated with a high road density, traffic flow, and vehicle start–stop frequency [ 6 , 29 ]. As was the smallest pollutant in the study area.…”

Section: Resultsmentioning

confidence: 99%

“…The spatial distribution pattern of NPI was similar to that of Hg, Pb, and Cu, which indicates that the spatial distribution pattern of NPI was mainly influenced by these elements. According to the field research and data analysis of the study area, high Pb, Cu, and Hg concentrations in USD mainly occur in the city center near the main urban roads, residential and commercial areas, and in the northeastern industrial area, which indicates that transportation, industrial emissions, and commercial activities may be the main sources of HMs in the USD of the study area [ 6 , 106 , 107 ]. In general, these elements in the USD of the study area were affected by human activities to varying degrees.…”

Section: Resultsmentioning

confidence: 99%

“…Urban surface dust (USD) is a pollutant consisting of one or more organic and inorganic particles with strong environmental indicator effects, and its sources are mainly influenced by a combination of natural and anthropogenic factors, such as industrial emissions, transportation, urban construction, municipal emissions, atmospheric deposition, and soil erosion [ 1 , 2 , 3 , 4 , 5 , 6 ]. USD with high concentrations of heavy metals (HMs) poses serious risks to urban ecology and human health [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Heavy Metal Pollution and Risk Assessment of Surface Dust in the Arid NW China

Yang

Eziz

Hayrat

et al. 2022

IJERPH

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High concentrations of heavy metals (HMs) in urban surface dust (USD) can be extremely hazardous to urban ecology and human health. Oasis cities are located at the edge of deserts and are more exposed to salt/sandstorms, and they face a significantly higher accumulation of USD than wet or semi-humid areas. However, systematic studies on the pollution and risk assessment of HMs in USD in oasis cities have rarely been conducted. This study systematically analyzed the enrichment status, spatial distribution, pollution levels, health risks, and sources of HMs in USD in a typical oasis city (Changji city). The results showed that the average concentrations of Pb, Ni, As, Cd, Hg, and Cu in the USD of Changji city were 46.83, 26.35, 9.92, 0.21, 0.047, and 59.33 mg/kg, respectively, and the results of the pollution index evaluation showed moderate Pb, Hg, and Cu pollution, mild Cd pollution, and no Ni or As pollution. The spatial distribution of HM concentrations in the USD was substantially heterogeneous. High values of Pb, Hg, and Cu concentrations were mainly observed in areas with relatively intensive transportation and commercial activities, and high values of Cd and Ni were observed in industrial areas. The health risk assessment showed that HMs do not pose non-carcinogenic risks to humans at their current level, but they pose a carcinogenic risk to children, with As contributing the largest carcinogenic and non-carcinogenic risks. The source identification of HMs showed that the main pollution of HMs were traffic sources for Pb and Cu, industrial sources for Ni and Cd, natural sources for As, and coal-fired sources for Hg. According to the results of the quantitative analysis with the positive matrix factorization, the contribution of pollution sources followed this order: industrial sources (31.08%) > traffic sources (26.80%) > coal-fired sources (23.31%) > natural sources (18.81%).

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Heavy Metal Pollution and Risk Assessment of Surface Dust in the Arid NW China

Yang

Eziz

Hayrat

et al. 2022

IJERPH

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“…Several towns and hill stations lying in the Himalayan range are tourism hotspots and garner lakhs of tourists. The heavy influxes of tourist activities cause a hike in particulate matter (PM) emission, which poses an imbalance to the ecosystem (Gautam et al 2022a&b; Gupta et al, 2022; Morakinyo et al, 2017). The increased concentration of PM is hazardous as it directly or indirectly affects human life by impacting the pulmonary system and causing various health issues (Chelani & Gautam, 2022; Harr et al, 2022; Kumar, Samuel, et al, 2022; Wang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Micro‐ to macro‐scaling analysis of PM_2.5 in sensitive environment of Himalaya, India

Kimothi,

Chilkoti,

Rawat

et al. 2023

Geological Journal

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Fine particulate matter (PM) in the atmosphere has become a significant air contaminant with substantial health consequences. Although airborne remote sensing and ground sensor monitoring can offer air quality datasets containing PM2.5, there are limitations to effectively analysing large long‐term datasets. The research aims to evaluate air quality over the Himalayan region using the multifractal approach for the PM2.5 data time series. Fractal dimension (FD), Hurtz exponent (H), and predictability (PI) are estimated using the rescaled range. PM2.5 is found to have high concentration and frequency throughout the day. The same is found in the night hours during peak tourism months. The hourly PM2.5 time series datasets have shown multifractality. The primary reason for this is emissions produced by vehicles and anthropocentric activities in the region. The H is used to assess the dynamic features of the PM2.5 time series in terms of persistence and self‐correlation. In the context of climate change studies, it is crucial to monitor the spatial distribution and dynamic behaviour of PM2.5 in the Himalayan foothills. This study aims to provide prediction analyses and air quality index (AQI) estimates and demonstrate how PM2.5 concentrations alter the sensitive environment throughout the micro to macro scale. This will help us to build a long‐term strategy for reducing the harmful effect of increasing pollution levels on the ecosystem and human health.

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“…Spatio temporal analysis refers to the exploration of any information relating to space and time (Gudmundsson et al 2017). Determining the spatial distribution of an air, soil or waterborne contaminant by its abiotic factors can help to develop an understanding of its background sources and influencing factors and is important for environmental and health risk evaluation (Ambade et al 2021a(Ambade et al ,b, 2022aBisht et al 2022;Gupta et al 2022;Ambade and Sethi, 2021;Kurwadkar et al 2021;Maharjan et al 2021). This explains the recent expansion of research on the spatiotemporal analysis of PM i (Deng et al 2022;Han et al 2022;Li et al 2022;Wang et al 2022a,b;Zhang et al 2022;Zhao et al 2022;Owoade et al 2021;Zhou and Lin 2019).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal statistical analysis of PM1 and PM2.5 concentrations and their key influencing factors at Guayaquil city, Ecuador

Rincón

Morantes

Roa-López

et al. 2022

Stoch Environ Res Risk Assess

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Guayaquil, Ecuador, is in a tropical area on the equatorial Pacific Ocean coast of South America. Since 2008 the city has been increasing its population, vehicle fleet and manufacturing industries. Within the city there are various industrial and urban land uses sharing the same space. With regard to air quality there is a lack of government information on it. Therefore, the research’s aim was to investigate the spatio-temporal characteristics of PM1 and PM2.5 concentrations and their main influencing factors. For this, both PM fractions were sampled and a bivariate analysis (cross-correlation and Pearson's correlation), multivariate linear and logistic regression analysis was applied. Hourly and daily PM1 and PM2.5 were the dependent variables, and meteorological variables, occurrence of events and characteristics of land use were the independent variables. We found 48% exceedances of the PM2.5-24 h World Health Organization 2021 threshold’s, which questions the city’s air quality. The cross-correlation function and Pearson’s correlation analysis indicate that hourly and daily temperature, relative humidity, and wind speed have a complex nonlinear relationship with PM concentrations. Multivariate linear and logistic regression models for PM1-24 h showed that rain and the flat orography of cement plant sector decrease concentrations; while unusual PM emission events (traffic jams and vegetation-fires) increase them. The same models for PM2.5-24 h show that the dry season and the industrial sector (strong activity) increase the concentration of PM2.5-24 h, and the cement plant decrease them. Public policies and interventions should aim to regulate land uses while continuously monitoring emission sources, both regular and unusual.

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Spatial distribution, pollution levels, and risk assessment of potentially toxic metals in road dust from major tourist city, Dehradun, Uttarakhand India

Cited by 25 publications

References 69 publications

Heavy Metal Pollution and Risk Assessment of Surface Dust in the Arid NW China

Heavy Metal Pollution and Risk Assessment of Surface Dust in the Arid NW China

Micro‐ to macro‐scaling analysis of PM_2.5 in sensitive environment of Himalaya, India

Spatio-temporal statistical analysis of PM1 and PM2.5 concentrations and their key influencing factors at Guayaquil city, Ecuador

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Spatial distribution, pollution levels, and risk assessment of potentially toxic metals in road dust from major tourist city, Dehradun, Uttarakhand India

Cited by 25 publications

References 69 publications

Heavy Metal Pollution and Risk Assessment of Surface Dust in the Arid NW China

Heavy Metal Pollution and Risk Assessment of Surface Dust in the Arid NW China

Micro‐ to macro‐scaling analysis of PM2.5 in sensitive environment of Himalaya, India

Spatio-temporal statistical analysis of PM1 and PM2.5 concentrations and their key influencing factors at Guayaquil city, Ecuador

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Micro‐ to macro‐scaling analysis of PM_2.5 in sensitive environment of Himalaya, India